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Measuring urban segregation based on individuals daily activity patterns: A multidimensional approach
Abstract
This paper develops a methodology to measure urban segregation based on individuals’ sociospatial experience of daily life. Since segregation can be considered as the isolation of people from those unlike themselves, its degree increases with the similarity in ethnicity, economic status, or other sociodemographic dimensions of interest between individuals and people who they are exposed to in their daily usage of urban space. Based on this perspective, we propose a regression estimator that measures segregation by assessing similarity or likeness between people and the social environments they experience in daily activity spaces. Compared to traditional segregation measures, the proposed estimator is not restricted to measuring residential segregation, but recognizes and assesses segregation as a dynamic process that unfolds in the daily life routines of individuals in a society and depends on the different ways individuals or social groups use urban space. It can be applied to various segregation factors, categorical or continuous, as well as to examine their interactions in a society. An empirical study in Hong Kong is used to demonstrate the proposed approach.
... Under the paradigm of time geography and behavioral geography, activity space refers to the collection of locations that individuals are directly in contact with in their daily lives (Golledge and Stimson, 1997). Scholars believe that activity space-based segregation research can help analyze sociospatial interactions in different geographical contexts (Wong and Shaw, 2011), reveal the social isolation that disadvantaged groups suffer from in their daily lives (Schönfelder and Axhausen, 2003), and understand the mechanism of urban spatial patterns on the availability of social resources to individuals (Li and Wang, 2017;Ta et al., 2021a). Activity space-based segregation research forms a people-based sociospatial segregation measure, which helps expand the framework of mainstream theories. ...
... Recent studies have considered the social environment to which individuals are exposed in their activity space (Li and Wang, 2017), which is called social exposure. The underlying assumption is that opportunities for cross-group interactions depend on population composition in an individual's social environment (Hägerstrand, 1970). ...
... Since then, scholars have measured activity space-based segregation levels in different Chinese cities (Tan et al., 2022;Wang and Li, 2016;Zhang et al., 2019), which has promoted the development of activity space-based segregation measures. These studies are based on both small data, such as questionnaires, and big data (Li and Wang, 2017;Zhang et al., 2022), enriching the data applications in related fields. Second, scholars have analyzed the diurnal and day-to-day changes in activity space-based segregation in Chinese cities (Xian et al., 2022;Zhang et al., 2019), addressing the temporal dynamics in activity spacebased segregation studies. ...
Measuring and explaining sociospatial segregation is essential in urban and social geography. Recent advances in activity space-based segregation provide new opportunities to study sociospatial segregation. This paper provides a comprehensive review of the emerging activity space-based segregation research in terms of measurements, dimensions, and influential factors. We highlight the trend toward integrating spatial, temporal, and social dimensions in activity space-based segregation measurement. Then, a multidimensional framework is constructed to cover the spatial form, opportunity exposure, spatiotemporal interaction, and social relationship of activity space-based segregation research. This paper ends with challenges and future directions for activity space-based segregation research, highlighting the importance of the temporal dimension, social interaction, influential mechanisms, and social effects.
... The earlier studies relied on specifically designed small-scale surveys, asking about respondents' everyday activity locations (Scheiner, 2000) or time spent in different sociocultural spaces (Schnell and Yoav, 2001). In more recent studies (Wang et al., 2012;Li and Wang, 2017), respondents were asked to fill in travel diaries. In addition, scholars have made use of extensive neighborhood/household (travel) surveys, conducted by state or regional authorities, that include information on respondents' key destinations (Jones and Pebley, 2014;Browning et al., 2017) or daily mobility (Wong and Shaw, 2011;Le Roux et al., 2017). ...
... Additional data were more often used in studies employing big data than in studies using traditional data sources-80 and 54% of studies, respectively. Almost half of those studies used census data in addition, for example, to obtain neighborhood characteristics for examining individuals' exposure to different socio-economic contexts within one's activity space (Jones and Pebley, 2014;Li and Wang, 2017). Eight studies applied mixed methods in examining activity space segregation, e.g., by combining quantitative activity space analysis based on GPS tracking or survey data with a qualitative analysis based on interview data (Scheiner, 2000;Shdema et al., 2018). ...
... Interestingly, China (n = 6) and Estonia (n = 5) stand out with a number of studies (Figure 4). In China, studies focused on income groups (Zhou et al., 2015), and people residing in a range of housing types (Li and Wang, 2017) or neighborhoods (Wang et al., 2012). The research in Estonia focused on the difference between language groups as proxies for ethnicity, based on mobile phone data (Järv et al., 2015;Mooses et al., 2016;Silm et al., 2018). ...
The activity space approach is increasingly mobilized in spatial segregation research to broaden its scope from residential neighborhoods to other socio-spatial contexts of people. Activity space segregation research is an emerging field, characterized by quick adaptation of novel data sources and interdisciplinary methodologies. In this article, we present a methodological review of activity space segregation research by identifying approaches, methods and data sources applied. First, our review highlights that the activity space approach enables segregation to be studied from the perspectives of people, places and mobility flows. Second, the results reveal that both traditional data sources and novel big data sources are valuable for studying activity space segregation. While traditional sources provide rich background information on people for examining the social dimension of segregation, big data sources bring opportunities to address temporality, and increase the spatial extent and resolution of analysis. Hence, big data sources have an important role in mediating the conceptual change from a residential neighborhood-based to an activity space-based approach to segregation. Still, scholars should address carefully the challenges and uncertainties that big data entail for segregation studies. Finally, we propose a framework for a three-step methodological workflow for activity space segregation analysis, and outline future research avenues to move toward more conceptual clarity, integrated analysis framework and methodological rigor.
... In the literature, place-based analysis transitions to peoplebased analysis, and scholars have made attempts to integrate them. Studies on socio-spatial segregation are evolving from focusing more on home neighborhoods and residential segregation to paying more attention to non-home stations, space-time paths, and mobility of people (Hägerstrand, 1985;Kwan, 2009;Wong and Shaw, 2011;Chai, 2013;Ahas, 2014a, 2014b;Li and Wang, 2016;Park and Kwan, 2018;Shoval et al., 2018). ...
... Recently, the analysis of socio-spatial segregation at the individual level has demonstrated promising progress, and the impacts of the practices, processes, and patterns of people's mobility are emphasized, reflecting on the new mobility narrative that the social sciences are facing today (Sheller and Urry, 2003;Farber et al., 2012;Raanan and Shoval, 2014;Silm and Ahas, 2014a;Li and Wang, 2016;Park and Kwan, 2018;Shoval et al., 2018). The mobility of people would impact their segregation in home and non-home stations (Farber et al., 2012;Park and Kwan, 2018) and the reconstruction of social relationships outside their home neighborhoods (Jaffe et al., 2012;Yip et al., 2016). ...
... However, among those studies that integrate time, space, mobility, and activity components in analysis, those measure socio-spatial segregation at the individual level are limited (e.g., Palmer, 2013;Silm and Ahas, 2014a;Yip et al., 2016;Park and Kwan, 2018). Aside from variables such as ethnic identity Ahas, 2014a, 2014b;Raanan and Shoval, 2014), social class, or housing tenure status (Murie and Musterd, 1996;Palmer, 2013;Li and Wang, 2016), the variables of age and occupation are not adequately explored (Park and Kwan, 2018). The variable of age could relate to life cycle issues, which are important in socio-spatial segregation theories, and the variable of occupation is associated with the difference among employed people and the unemployed, such as retired elderly and housewives whose mobility may be relatively low (Rofe, 2003). ...
Recent studies on socio-spatial segregation have revealed the uneven segregation experiences of individuals within their daily life contexts. However, little is known about its temporal variations across the week. The advancement of GIS and GPS tracking technology also poses methodological challenges in processing rich mobility–activity data efficiently in identifying the socio-spatial segregation patterns. With data collected by a mobile phone app that ran on the participants' mobile phone for a whole week, this paper integrates the spatial, temporal, mobility, and activity dimensions with the demographic data to segregation patterns of the participants and assesses segregation at the individual level. Our findings indicate that the socio-spatial segregation level decreased in the daytime and increased at night, and this pattern was consistent across a week. However, no significant differences are found between different age groups, occupation, housing types and home neighborhood types. To improve the efficiency of data processing, this paper employs decision tree algorithms supplemented by the analysis of variance and Tukey's honestly significant difference test to identify meaningful mobility–activity patterns with significant intergroup differences. It is able to pinpoint temporal and spatial activity-mobility patterns that crosscut home location, location of workplace, and socioeconomic status. It also helps connect residential segregation and segregation that goes beyond the home neighborhoods.
... Measuring segregation in large geographic areas is more likely to show a higher level of overall integration than measuring it in smaller geographic units [2]. Another approach in the same line is that conducted by Li and others [52]. This work develops a methodology to measure urban segregation based on the socio-spatial daily experience of individuals in Hong Kong. ...
... Therefore, some studies have associated the concept of daily mobility and activity space with the experience of social segregation, isolation or exclusion of individuals in urban space. Activity space, which encompasses the space that individuals visit and use when engaging in everyday activities [47], Golledge and Stimson captures the physical environment in which exposure and potential social interactions can take place [52]. Taking the above into consideration, we consider daytime segregation as "the level of geographic agglomeration of people of different social status at their place of visitation or work". ...
... Furthermore, the research done by Xia, and others, on five megacities in China studied the relationship between urban vitality-measured by small business transaction data and nighttime lighting-and contemporary compact city characteristics, such as mixed land use and high density, finding a significant positive spatial autocorrelation between urban land use intensity and urban vitality [32]. Moreover, the study by Li, and others, for the city of Shangzhen in China focused on measuring the relationship between morphology and urban vitality [52]. They found that a dense street grid, small to medium-sized blocks and the diversity and intensity of construction and land use are beneficial to urban vitality. ...
Latin American cities are known for their high levels of marginality, segregation and inequality. As such, these issues have been the subject of substantial discussions in academia, with the predominant approach being the study of residential segregation, or what we call “nighttime segregation”. Another dimension of urban sociability, related to labor, is what we call “daytime segregation”, which has been far less studied. This article makes an original methodological contribution to the measurement of non-residential or daytime segregation based on data from mobility surveys. It seeks to explain this segregation measurement according to the diversity and distribution of land uses, as well as other characteristics of the built stock, such as land price and built-up density. We measured daytime social mix in urban spaces, and we show how it highly relates to land use diversity in a Latin American megacity, such as Santiago, Chile. We found that land use diversity plays a key role in enhancing the daytime social diversity of urban spaces, contributing to generate a more heterogeneous city and social gatherings during working days. This research is not only a contribution to the understanding of sociability patterns in cities but is also a contribution to public policy and the work of urban planners, as it informs the development of more diverse and integrated cities, which is a key tool for strengthening democracy, the exchange of ideas, the economy and social welfare.
... Many of these studies investigated segregation under the framework of time geography, utilizing individual spatiotemporal trajectories to measure segregation. Some methods include comparing spatiotemporal trajectories (Atkinson & Flint, 2004), community-based random walk analysis (Dannemann, Sotomayor-Gómez, & Samaniego, 2018), developing single segregation measures (Wong & Shaw, 2011), and applying regression-based measures (Li & Wang, 2017). However, these studies considering multiple sociogeographical spaces focused on racial-ethnic segregation exclusively. ...
... Traditional data sources for time geography rely on surveys as such travel diaries (Buliung & Kanaroglou, 2006;Li & Wang, 2017;Park & Kwan, 2018;Schönfelder & Axhausen, 2003;Wong & Shaw, 2011), which are usually small in size. Recent data depicting individual trajectories, such as cell phone data (Blumenstock & Fratamico, 2013;Dannemann et al., 2018;Järv, Müürisepp, Ahas, Derudder, & Witlox, 2015;Silm & Ahas, 2014), and location-based social network data (Phillips, Levy, Sampson, Small, & Wang, 2020;Wang, Edward, Small, & Sampson, 2018) are usually large in volumes. ...
... While using evenness (and/or clustering) measures to evaluate income distribution across neighborhood is reasonable, such approach focuses entirely on the static residential distribution of people labeled by their income levels, ignoring their potential interaction with different income groups in other socio-geographical spaces. In the context of measuring segregation that accounts for the interaction or mobility patterns of population subgroups, measures capturing the exposure dimension have been the preferred choices of measures (Kwan, 2013;Li & Wang, 2017;Wong & Shaw, 2011). Thus, evaluating income segregation based on a single index, or multiple indices of a single dimension has significant limitations if both the income differences and cross income-group interaction need to be considered. ...
As most studies of segregation rely on the evenness dimension, this current study proposes a graph embedding approach to explore the usefulness of employing the isolation-exposure dimension to evaluate income segregation. While most segregation studies analyzed the static distribution of population subgroups, current study attempts to classify neighborhoods based on house value as a proxy of income, residents' exposure to people of different income levels as constrained by their mobility patterns, and amenities available in the neighborhood. This study exploits the graph embedding method to classify neighborhoods by combining their various attributes, static population distribution and mobility data provided by smart cards to analyze income segregation in Shenzhen, China. Results identify four types of communities with different economic statuses, mobility patterns, and amenity characteristics. They provide rich descriptions about the connections between income segregation patterns, population dynamics, and neighborhood characteristics. The study found that the more segregated communities, which are composed of the poorest and richest groups, are mostly in the peripheral regions of the city while the inner city has lower levels of segregation, mainly due to differentials in transit accessibility. The study demonstrates the great potential of the proposed method to incorporate multiple aspects to evaluate segregation.
... While early studies of migrant segregation focused on residential neighborhoods, scholars have highlighted the importance of segregation in people's daily activities and travel behaviors in recent years (Kwan, 2013;Li & Wang, 2017;Schnell & Benjamini, 2005;Van Kempen & Wissink, 2014). Although a person's residential location is important as an anchor in his/her daily life, it is not adequate for revealing the whole picture of socio-spatial segregation because people also conduct many of their daily activities in places other than their residential neighborhoods (Park & Kwan, 2018;Wong, 2016). ...
... Unlike the racial/ethnic divisions in Western countries, migrant segregation in China is based mainly on the rural-urban and local-nonlocal divisions structured by the hukou system (Chan & Buckingham, 2008;Wu & Logan, 2016). Due to the institutional barriers of hukou and socioeconomic disadvantages, migrants are often excluded from public housing, better employment opportunities, and welfare services (Huang & Tao, 2015;Li & Placier, 2015;Li & Wang, 2017). While there is a large body of literature on residential and employment segregation (Li & Wu, 2008;Wang et al., 2010;Zhou et al., 2019), little is known about the segregation experiences of Chinese migrants in their daily lives (Lin & Gaubatz, 2017;Liu et al., 2019). ...
... To obtain a comprehensive evaluation of residents' activity space-based segregation in both the spatial and social dimensions, it is necessary to consider the importance of diverse locations visited in residents' daily lives. In addition, while some existing studies have noted the constrained spatial context of activity space and the influence of neighborhood environment on them (e.g., Li & Wang, 2017;Tana et al., 2016;Tao et al., 2020), their results are not entirely consistent. This paper will contribute to the literature on activity space by investigating the relationship between activity space-based segregation and neighborhood attributes in Shanghai, China. ...
The socio-spatial segregation experienced by migrants has attracted considerable attention and an increasing number of studies have examined segregation in migrants’ daily activity space recently. However, research on activity diversity and spatial contact between local residents and migrants has been limited. This paper fills this knowledge gap by investigating the differences in the extensity, intensity, diversity and exclusivity of activity spaces among local residents, urban migrants and rural migrants based on their routine activities in suburban Shanghai, China. It finds that rural migrants have low daily mobility and are physically constrained, and there is spatial sorting of activity locations among different social groups. Neighborhood environment significantly influences activity space-based segregation: People who live in neighborhoods with higher POI density and better access to commercial establishments and public spaces have small activity spaces, while those who live in neighborhoods with mixed land use, better access to public transit, and higher street connectivity have more diverse activity participation. Neighborhoods with better public spaces and a lower land use mix promote shared activity spaces. This study uncovers the segregation suffered by migrants by examining the usage of urban space and spatial interactions among social groups, enhancing our understanding of activity space-based segregation in developing countries.
... People experience segregation not only in their residential places but also in other places where they undertake daily activities, such as the workplace and sites for nonwork activities (Åslund & Skans, 2010;Lee et al., 2008;Lee & Kwan, 2011;McQuoid & Dijst, 2012;Palmer, 2013;Schwanen & Kwan, 2012;Wang, Li, & Chai, 2012). Therefore, residence-based segregation research ignores a considerable part of people's daily experiences, which might exacerbate or reduce the overall segregation people experienced (Jones & Pebley, 2014;Li & Wang, 2017;Wong & Shaw, 2011). Further, it is misleading to think that segregation in urban space is static (Ellis, Wright, & Parks, 2004;Kwan, 2013). ...
... In the past few years, an increasing number of studies have argued that segregation studies should be extended conceptually from residential segregation to spatiotemporal segregation of social groups in activity space, which refers to the segregation in the urban spaces where different social groups frequently visit and perform daily activities (Atkinson & Flint, 2004;Ellis et al., 2004;Li & Wang, 2017;Palmer, 2013;Schnell & Yoav, 2001;Wang et al., 2012;Wang & Li, 2016;Wong & Shaw, 2011). Most studies that adopted this conceptual perspective utilized the differences in the spatial characteristics of people's actual activity space (AAS) to identify the segregation among social groups, which revealed how different social groups are constrained by the size of activity space and the number and diversity of urban opportunities in their activity space (Järv et al., 2015;Patterson & Farber, 2015;Schönfelder & Axhausen, 2003). ...
... On one hand, people's activity spaces reflect the geographic areas they experience and the people they interact with in their daily life. The more an individual meaningfully interact with people with different social identities, the less the person is segregated (Li & Wang, 2017;Wong & Shaw, 2011;Park & Kwan, 2018). It is reasonable to expect that social segregation would be exacerbated if different social groups have systematically different activity spaces, because it means that they have different daily-life territories and less chance to interact with each other (Wang et al., 2012;Shareck et al., 2014;Jones & Pebley, 2014;Krivo et al., 2013;Park & Kwan, 2018). ...
Given the ongoing “mobility turn” in social science research, a more comprehensive understanding of segregation is needed. Activity-space-based segregation studies have aroused renewed interests in geography and urban planning research. Most of the existing studies utilized the differences in spatial characteristics of people's actual activity space to identify the segregation among social groups. However, few studies have examined activity-space-based segregation in terms of individuals' potential activity space and the temporal variations in their segregation experiences. This paper aims to help fill these two research gaps by implementing an empirical study in Beijing. We examine the activity-space-based segregation of the residents of different types of housing in a Beijing suburb in terms of both actual activity space and potential activity space. We further investigate the temporal variations of the residents' segregation experiences during a week. A 7-day individual GPS tracking dataset, combined with the activity diary data and the socioeconomic attributes data of 422 participants is used for the study. The major finding is that residents of different types of housing in Beijing do experience activity-space-based segregation, while the characteristics of segregation vary with respect to actual activity space and potential activity space. Also, participants' segregation experiences varies between different days of the week. This paper offers some empirical evidence on enriching the understanding of activity-space-based segregation research as well as improving the understanding about social segregation among the residents of different types of housing in Chinese cities. It also generates some nuanced knowledge for future policy recommendations in a broad context.
... The notion of "activity space segregation" was developed to reveal the full spectrum of the segregation of social groups in the course of people's daily lives, which depends not only on where they go but also when they go, how much time they spend there, and whom they may encounter (Kwan, 2013;Palmer, 2013;Schönfelder & Axhausen, 2003;Wong & Shaw, 2011). Despite this progress, most of the previous works have focused only on specific aspects of activities (e.g., only working, shopping, attending school, or traveling) (Silm & Ahas, 2014;Toger et al., 2023;Zhou et al., 2021), or take all the activities into consideration without classification when measuring the segregation level (Li & Wang, 2017;Wong & Shaw, 2011;Zhang et al., 2022). A very small number of works have attempted to clarify the relationship between segregation in different activity spaces (Hu et al., 2022;Toomet et al., 2015), but even these pioneering studies have shortcomings in terms of the classification of systematic activity types, the sampling size, or analysis granularity. ...
... Some studies have utilized a series of indicators (e.g., usage of time and space, number of activity locations, and geographical distribution of activity locations) to compare the spatial range and spatialtemporal modes of activities and thereby measure the social-spatial segregation of different social strata (Giannotti et al., 2021;Järv et al., 2015;Ö sth et al., 2018;Wang et al., 2012;Wang & Li, 2016). Other studies measured segregation from the viewpoint of individuals' potential contact with other social groups in their activity space (Farber et al., 2015;Li & Wang, 2017;Wong & Shaw, 2011;Yip et al., 2016). ...
This study compares place-based and individual-based segregation under different contexts in the Tokyo metropolitan area. Segregation level varies with individuals’ household income and activity space type. Segregation level is lower in the working and routine activities space than in residence and nonroutine activities space. The higher the individuals’ income, the lower the segregation level. This study reveals variations in segregation across activity contexts and how these differences distributed in urban areas.
... First, out-of-home activities are more integrated between different groups than their residences ( € Osth, Shuttleworth, and Niedomysl 2018). In other words, human mobility outside the residence reduces both racial and socioeconomic segregation (Silm and Ahas 2014b;Shelton, Poorthuis, and Zook 2015;Li and Wang 2017). ...
... Despite the large scale, these official data do not offer the spatial-temporal resolution needed to discern the activity space locations, especially for the dynamic locations of free-time activities. The second type is activity diary surveys (Li and Wang 2017;Park and Kwan 2018). Due to the small sample size, conclusions from such studies can hardly be generalized to the whole population. ...
The segregation–crime relationship is a classic topic in sociology and crime geography, yet existing literature mainly focuses on the impact of racial segregation at the global scale. Little is known about the impact of local segregation of other socioeconomic characteristics such as education level, an important segregation factor for racially homogenous countries like China. Also unknown is their impact beyond the residential domain. Using the Baidu Map Location-Based Service population data set and court records in 863 local geographic units of the central urban area of Beijing during 2018 and 2019, this study uncovers the spatial pattern of segregation between people with and without a bachelor’s degree measured in the residential space and activity space and further investigates the influence of these two types of educational segregation and their interaction effects with social context on theft and violent crime. Results show less segregation in the activity space than in the residential space. Both types of segregation, however, significantly increase the risk of theft and violence, with activity space–based segregation more consequential. Moreover, the positive segregation–crime link is moderated by the local social context measured by the educational composition among residents and the ambient population. Compared with residential segregation, activity space–based segregation is more detrimental for places dominated by the less educated. Our results highlight the elevated influence of segregation on safety beyond the residential space, especially for areas clustered with the less educated ambient population.
... Our data includes wide coverage and fine-grained spatiotemporal information, which enables a thorough examination of the dynamic segregation status of the entire central urban area. Further, we focus on social segregation based on educational attainment rather than race or ethnicity as most western studies underscore, since educational segregation is far more pronounced than racial or ethnic segregation in most Chinese cities (Li and Wang, 2017;Zhang et al., 2019aZhang et al., , 2019bShen and Xiao, 2020). ...
... In addition to specific life domains, activity-space-based segregation in Chinese cities has recently become a focus of research interest. At the global level, although residents of private housing and residents of public housing in Hong Kong experience less segregation in activity spaces than in residential realms (Li and Wang, 2017), socioeconomic segregation persists in people's activity spaces (Wang and Li, 2016;Yip et al., 2016). In Beijing, the upper class retreat from public space to avoid urban problems like environmental pollution or security risk, thereby initiating and perpetuating trajectories of segregation (Wang et al., 2012). ...
The literature on segregation is focused on the residential domain considered from a static perspective. In contrast, the purpose of our study is to examine temporal variations in the overall degree and spatial pattern of activity-space-based social segregation around the clock on weekday and at weekend in the central urban area of Beijing, China. Drawing on location-based service (LBS) big data, we measure the level of activity-space-based segregation at each hour of a weekday and a weekend day between groups of people who differ from each other in relation to formal educational achievements. Their comparisons with the segregation in major life domains such as residence and workplace are also made. At the global level, the extent of activity-space-based segregation fluctuates around the clock, with less segregation during the daytime than at night and less segregation on the weekend day than on the weekday. The segregation degrees for all groups are in descending order workplace segregation, residential segregation, and out-of-home non-employment segregation. At the local level, the highly segregated units centralize to city center in the morning and decentralize to suburban areas in the evening. The spatial segregation patterns at various times of the day change to a much greater extent on the weekday than during the weekend day, especially for employment centers and large-scale residential communities. Lastly, a spatial unit classification framework of real-time activity-space-based segregation is proposed to integrate multiple kinds of information pertaining to the segregation level and the dominant group in a given area at a given time with the extent and trend of the temporal variation identified presented as a concise map useful both to advancing further research and guiding policy formulation.
... As observed in Zhang et al. (2018), " people experience segregation not only in their residential places, but also in other places where they undertake daily activities, such as the workplace and sites for non-work activities". Thus a growing number of studies compare the spatial characteristics of activity spaces to assess segregation between social groups (Janelle, Goodchild, 1983;Schnell, Yoav, 2001;Schönfelder, Axhausen, 2003;Atkinson, Flint, 2004;Ellis et al., 2004;Wong, Shaw, 2011;Järv et al., 2015;Li, Wang, 2017;Demoraes et al., forthcoming). To do so, scholars call on a wide variety of methodological approaches (general linear model, composite indices, regressions, location quotient, centrographic analysis, exposure and dissimilarity measures, etc.). ...
... This may be attributable to the disciplines in which their authors work (maps tend to be more frequent in studies by geographers), and also by the fact that the data tends to refer to the individual level, making it hard to synthesize on a map. Several scholars draw on aggregate data for spatial units (Ellis et al., 2004;Wong, Shaw, 2011), and some studies do not provide any sort of map (Åslund, Skans, 2010;Atkinson, Flint, 2004;Li, Wang, 2017). The study zones are also highly variable, ranging from entire countries, to groups of cities, a single entire city, or several districts. ...
The purpose of this study is to understand changes to household members’ access to place of activity, against the backdrop of urban and socio-demographic transformation. In particular, it seeks to establish whether adults are currently disadvantaged by residing in a location which, in terms of daily mobility, is better suited to their children, or whether other factors are at work, depending particularly on the household’s position in the social hierarchy. It further seeks to understand how mobility conditions have changed over time depending where individuals reside in the city. To this end, it puts forward a new concept called “space-time of action” and proposes a way of mapping it. Drawing on data from two surveys in Bogotá (Colombia) in 1993 and in 2009, “space-times of action” are here used to study joint changes to co-residing working adults’ and children’s access to place of work or study. It details how the space-times of action were devised, before then discussing their advantages and limitations.
... Nightingale (2012) considers segregation as old as Eridu, which is a 7000-year-old city, but the term did not exist until the 1890s when city splitters started to use it. Since the emergence of this word, many studies have been done in American and European countries like the US and England (See for example Boschman, 2012;Catney, 2016;Feitosa & Wissmann, 2006;Glynn, 2010;Massey & Denton, 2001) and some Asian countries like China in East Asia and India in South Asia (See for example Li & Wang, 2016;Liu, Huang, & Zhang, 2017;Roy, Lees, Pfeffer, & Sloot, 2018) but few studies have been conducted in Western Asia, specifically Iran in this regard. ...
... The deeper and closer to reality the scholars' understandings are, the more reliable are the studies and their following recommendations for policymaking. Segregation studies in American, European and Asian cities are usually followed with the same approach (Andersson & Hedman, 2016;Douds & Wu, 2017;Li & Wang, 2016;Liu et al., 2017;Usher, Gaskin, Bower, Rohde, & Thorpe Jr, 2016;Wichowsky, 2017;Zhao & Wang, 2017) where class and racial segregation are deliberate and connected (Redford, 2016), and their consequences are often studied through inferential statistics, and less attention is given to the pure experience of people. To fill this gap, this study tends to provide some evidence from an Iranian context named Razavieh -a segregated neighborhood in Tehran -through exploring residents' experiences. ...
The purpose of this article is to understand segregation consequences in one of the Tehran metropolis neighborhood through studying the lived experiences of segregated individuals. This study tries to examine the relationship between segregation as an urban phenomenon and residents instead of studying segregation solely. It investigates the lived experiences of residents who have been living in Razavieh as one of Tehran segregated neighborhoods through unstructured interviews. Findings suggest that Razavieh residents are usually segregated there involuntarily. Living in Razavieh as a segregated setting has affected residents' lives through managerial, economic, social, physical and infrastructure consequences. Residents' talks address a subjective belief among them called social distance which exists between them, citizens of other neighborhoods and city officials. Conceptions also indicate that the first two categories of consequences lead to the last three, and social consequences are more tangible than others. Surprisingly, the role of some institutions and their satisfactory performance in Razavieh has led to the social cohesion of people who are living there. In total, individuals' experiences reveal some hidden points of a phenomenon which may not be revealed through other methodologies.
... Many segregation studies use predefined areas such as census boundaries to define neighborhoods and measure the nature and extent of urban segregation (Omer and Benenson 2002;Noonan 2005;Lloyd 2010;Wong and Shaw 2011;Weaver 2015;Li and Wang 2017;Merrilees et al. 2017). Grannis (2009) stressed that although boundaries defined by census or other administrative agencies generate statistical units that are useful for summarizing data, they do not delineate neighborhoods in a socially meaningful way or account for the potential for residents to interact. ...
... Regardless of how neighborhoods are defined, many segregation studies focus exclusively on residential segregation, often using census data to define the ethnic mix of the residential population (Grannis 1998;Hughes et al. 2007;Lloyd and Shuttleworth 2012;Bruch 2014). Although some studies have measured use of space through activity diaries (Wong and Shaw 2011;Farber et al. 2013;Li and Wang 2017) or from mobile phone usage (Silm (Palmer et al. 2013;Roulston et al. 2017). GPS tracks potentially identify locations that people visit when not at home, as well as the routes people take to reach these locations. ...
Long-standing tensions between Protestant and Catholic communities in Northern Ireland have led to high levels of segregation. This article explores the spaces within which residents of north Belfast move within everyday life and the extent to which these are influenced by segregation. We focus in particular on the role that interconnecting tertiary streets have on patterns of mobility. We adapt Grannis’s (1998 Grannis, R. 1998. The importance of trivial streets: Residential streets and residential segregation. American Journal of Sociology 103 (6):1530–64.[Crossref], [Web of Science ®] , [Google Scholar]) concept to define T-communities from sets of interconnecting tertiary streets within north Belfast. These are combined with more than 6,000 Global Positioning System (GPS) tracks collected from local residents to assess the amount of time spent within different spaces. Spaces are divided into areas of residents’ own community affiliations (in-group), areas not clearly associated with either community (mixed), or areas of opposing community affiliation (out-group). We further differentiate space as being either within a T-community or along a section of main road. Our work extends research on T-communities by expanding their role beyond exploring residential preference, to explore, instead, networks of (dis)connection through which social divisions are expressed via everyday mobility practices. We conclude that residents are significantly less likely to move within mixed and out-group areas and that this is especially true within T-communities. It is also evident that residents are more likely to travel along out-group sections of a main road if they are in a vehicle and that women show no greater likelihood than men to move within out-group space. Evidence from GPS tracks also provides insights into some areas where mixing appears to occur. Key Words: GIS, Northern Ireland, postconflict, segregation, T-communities.
... Hence, activity space has become a crucial measurement index for socio-spatial segregation; it links travel behavior and socio-spatial segregation (Schönfelder & Axhausen, 2003). According to the spatial pattern of daily trips, socio-spatial segregation is decomposed into several dimensions, such as residential, working, and leisure segregation (Li and Wang., 2017;Toomet et al., 2015). Some scholars view activity space as an area for greater communication opportunities, and employ it to reveal the possibility of social exclusion (Tao et al., 2020). ...
As population aging has been an issue worldwide, the mobility of older people have attracted the attention of scholars from urban planning, transport geography, and social science. However, few have investigated socio-spatial differentiation among mobility groups, considering their daily needs and activity spaces. To fill this research gap, we conducted a comparative analysis of socio-spatial differentiation, based on individual activity spaces. We used smart card data from Kunming, China, to identify selected individuals' residential locations and travel patterns, and evaluate their accessed activity space. We performed a disaggregated analysis of the individual activity space, and then aggregated the activity counts on each grid. This study found that the residential locations of older metro travelers are significantly different from those of other metro travelers. In addition, socio-spatial differentiation was found to exist due to different daily requirements. The results were confirmed in three LASSO models with built environment variables. These findings are useful in urban and transportation planning to improve elder-friendly services.
... This approach was chosen since individuals' daily activities do not coincide with the administrative boundaries of their neighborhoods (Weng et al., 2019). The 15-minute neighborhood life circle represents an area reachable within a 15-minute walk, roughly equivalent to 1 km, following the road networks from the neighborhood (Li & Wang, 2017). This method accounts for the spatial range of activities performed by residents around the neighborhood to create a bespoke neighborhood area. ...
Recent decades have witnessed continued research interest in the residents' sense of community (SOC) towards their neighborhoods, attributed to the rapid global expansion of urban spaces and increased human mobility. Early studies predominantly focused on the influence of SOC from a place-centric perspective, emphasizing the physical attributes of neighborhoods. However, recent scholars have called for a shift in research towards an individual-activity-based approach. Despite this, there has been a relative dearth of knowledge regarding the nuanced activity factors and intrinsic pathways that impact SOC, particularly within mixed housing neighborhoods. To bridge this gap, this study utilized first-hand social survey data and daily activity diaries collected in Fuzhou to examine the associations between daily activities and SOC and identify the critical mediator. Multi-group Structural Equation Modeling (SEM) analysis revealed several noteworthy findings. Firstly, despite living together in a mixed neighborhood, affordable and market housing residents exhibited significant disparities in their daily activities, facility utilization, and SOC. Residents of market housing demonstrated lower SOC compared to those in affordable housing. Secondly, daily activities had differing impacts on SOC between these groups. A variety of activity factors, such as the proportion of activity space to the neighborhood, time allocated to local activities, and the diversity of activity types, collectively enhanced SOC. These effects were particularly marked within affordable housing. Conversely, the positive influence of daily social interactions on SOC was exclusive to residents of market housing. Additionally, the use of neighborhood facilities serves as an underlying mechanism. Frequent utilization of proximal facilities can substantially improve SOC among residents, particularly for those in affordable housing. These findings offer valuable insights for the planning and managing of mixed housing neighborhoods and may also inspire strategies in other neighborhood types.
... We defined the neighborhood area as the 15 min life circle area of each neighborhood, considering that people's daily activities cannot be divided according to the administrative boundaries of their neighborhoods [54,55]. The scope of the 15 min neighborhood life circle is a 15 min (approximately 1 km) walk from the neighborhood gate along the road [56]. Then, we calculated the proportion of overlapping spatial ranges of the neighborhood area and each resident's activity space and obtained the ASN indicator, which refers to the ratio of neighborhood space to individual daily activity space. ...
As a kind of urban neighborhood with strong internal heterogeneity, mixed-housing neighborhoods have attracted wide attention from scholars in recent years. Strengthening community ties in mixed-housing neighborhoods is of great significance for increasing neighborhood social capital, cultivating a sense of community, and promoting sustainable development of the neighborhood. The neighborhood activities of residents are an important factor in promoting community ties. However, different housing groups in mixed-housing neighborhoods may have differentiated or even segregated overall daily activities, which may impact their neighborhood activities and call for differentiated planning strategies. In this study, we conduct an empirical study in Fuzhou, China, to identify the spatiotemporal-behavior-based microsegregation and differentiated community ties between residents of different types of housing. The data were collected in 2021 and included residents’ activity diary data and questionnaire data about neighborhood interaction and community ties. Through an analysis of the daily overall activity space and activities within the neighborhood areas, the spatiotemporal-behavior-based social segregation of various housing groups is depicted. Furthermore, a multigroup structural equation modeling method was used to analyze the relationships among residents’ spatiotemporal behaviors, neighborhood interactions, and community ties, and the heterogeneous influence effects across housing groups. The results show that the more residents’ activity spaces overlap with the neighborhood area, the more out-of-home time they spend within the neighborhood, and that the more types of activities are conducted within the neighborhood area, the stronger their community ties are. In addition, neighborhood interaction played a linkage role in the relationships of residents’ spatiotemporal behaviors and community ties. Our research aims to further the understanding of microsegregation at the neighborhood level and provide references for the development of mixed-housing neighborhoods and urban land use.
... In recent years, an increasing number of studies have been conducted to examine people's utilization of different urban forms and sociospatial differentiation from the perspective of activity space [13,[23][24][25][26][27]. For instance, Kwan [28] found that women face higher levels of daytime fixity constraints when accessing jobs and urban opportunities. ...
Time allocation is closely related to life quality and is a potential indicator of urban space utilization and sociospatial differentiation. However, existing time allocation studies focus on how time is allocated to various activities but pay less attention to where individuals allocate their time. In the context of China’s transformation, this study examines the differences in time allocation in different urban spaces between low- and non-low-income groups based on two methods, descriptive statistics and social area analysis. The results show that low-income participants’ daily activities (especially work) are highly dependent on the central city area. However, they are at a disadvantage in accessing the central city area. Nevertheless, non-low-income individuals have diversified activity spaces and can better choose locations according to the purpose of activities and make fuller use of various types of urban areas. This study indicates that there are social differences in time allocation and urban space utilization among different income groups. The results obtained with regression models reveal that in addition to income, activity characteristics and built environment characteristics are significant factors affecting the differences. Social policies should support the equitable distribution of urban resources for different social groups, especially for vulnerable groups who live in affordable housing.
... For example, through examining the racial/ethnic composition of individuals' activity spaces, a study observed that African Americans prefer to visit locations with a higher proportion of their own group and a similar pattern for Latinos (Jones & Pebley, 2014). A regression estimator was designed to measure the similarity between people and the social characteristics they experience in daily activity spaces (Li & Wang, 2017). A social interaction potential metric based on the time-geography concept and home-work flow matrix was proposed to capture both with-group and between-group interaction potential (Farber et al., 2012). ...
Recent theoretical and methodological advances in activity space and big data provide new opportunities to study socio-spatial segregation. This review first provides an overview of the literature in terms of measurements, spatial patterns, underlying causes, and social consequences of spatial segregation. These studies are mainly place-centred and static, ignoring the segregation experience across various activity spaces due to the dynamism of movements. In response to this challenge, we highlight the work in progress toward a new paradigm for segregation studies. Specifically, this review presents how and the extent to which activity space methods can advance segregation research from a people-based perspective. It explains the requirements of mobility-based methods for quantifying the dynamics of segregation due to high movement within the urban context. It then discusses and illustrates a dynamic and multi-dimensional framework to show how big data can enhance understanding segregation by capturing individuals’ spatio-temporal behaviours. The review closes with new directions and challenges for segregation research using big data.
... As has already been addressed in the preceding section, Kwan (2009;, for instance, supports the measurement of exposure to others at the individual level, especially in order to study segregation in activity spaces. Li and Wang (2017) and Wang et al. (2012) have measured activity space segregation in terms of the correlation between the social characteristics of individuals and those of their daily activity spaces. ...
... A tailor-made mobile phone application may collect very detailed spatial and temporal data on activity spaces of individuals, supplemented by socioeconomic data on individuals (Yip, Forrest, & Xian, 2016). Nevertheless, even though it would automate data collection, it shares similar constraints with more "traditional" travel diaries (see Li & Wang, 2017) related to an extreme effort in contacting participants and a high data-collection cost. ...
Social segregation research has a long tradition in urban studies. Usually, these studies focus on the residential dimension, using official registries (e.g., census data), which show population distribution at night. Nevertheless, these studies disregard the fact that the population in cities is highly mobile, and its spatial distribution dramatically changes between night and day. The emergence of new data sources (Big Data) creates perfect conditions to consider segregation as a process, by providing the opportunity to dynamically analyse temporal changes in social segregation.
This study uses mobile phone data to analyse changes in social segregation between night and day. Our case study is Medellin (Colombia), a highly socially-segregated, South American city, where social integration policies are being developed, targeting the population in the most disadvantaged neighbourhoods. We use several complementary indicators of social segregation, supplementing them with mobility indicators that help explain changes in spatial segregation between night and day.
The main conclusion is that daily mobility reduces the concentration of a particular group within neighbourhoods and increases the degree of social mixing (exposure) in local settings. This greater social exposure softens local contrasts (outliers) and increases the extension of spatial clusters (positive spatial autocorrelation), so general clustering trends emerge more clearly. The study also makes clear that increased exposure during the day mainly occurs due to the mobility of the low-income population, who are the most likely to leave their neighbourhood during the day and who travel the greatest distances to the most diverse set of destinations.
... Although residential segregation is an important dimension of socio-spatial isolation, segregation may also manifest in spaces where people work, relax, and make social contacts. Thus, individuals may experience different levels of segregation in different activity spaces (Krivo et al., 2013;Kwan, 2013;Li & Wang, 2017). These observations have prompted studies to consider segregation as dynamic and multi-dimensional beyond residential space (Farber, Neutens, Miller, & Li, 2013;Schnell & Yoav, 2001;Zenk et al., 2011). ...
Due to the difficulty of tracking large numbers of new migrants, how their daily activity behaviors differ from those of settled residents has not been well investigated, leading to a lack of understanding of new migrants' integration. Meanwhile, existing research largely emphasized residential space and ignored other activity disparities. To obtain a more comprehensive picture of urban segregation, we identified new migrants and two settled urban groups from two kinds of human mobility data. A S‐T‐A‐D‐I interactive framework was proposed to measure segregation from multiple activity dimensions, including spatial colocation, temporal coexistence, accessibility, activity diversity, and social interaction. Two‐scale analysis of spatial colocation patterns reveals residential segregation by both residential location and housing type, suggesting the effectiveness of the mobility data in profiling socioeconomic groups. The temporal disparity in undertaking activities was unveiled by identifying temporal coexistence patterns. Moreover, the groups presented significant inequality in accessibility owing to the use of different travel modes, leading to a notable disparity in activity diversity. Jointly determined by the disparities in space, time, and diversity, the three groups generated a high level of self‐segregation, and new migrants and transit users presented very low interaction potentials with the car group.
... Further, studying location-connectivity as opposed to person-connectivity bypasses some of the obstacles that come with more traditional approaches. One such obstacle is that existing literature on segregation in activity spaces either relies on travel diaries and surveys [11,12], or on dense geospatial datasets that come from social media or mobile phones like the Twitter dataset that we use [3-5, 7, 8]. Survey-based methods produce rich data but are limited by sample size constraints and the reliability of respondents. ...
We present a novel metric for measuring relative connection between parts of a city using geotagged Twitter data as a proxy for co-occurrence of city residents. We find that socioeconomic similarity is a significant predictor of this connectivity metric, which we call “linkage strength”: neighborhoods that are similar to one another in terms of residents’ median income, education level, and (to a lesser extent) immigration history are more strongly connected in terms of the of people who spend time there, indicating some level of homophily in the way that individuals choose to move throughout a city’s districts.
... A better understanding of the potential of the Ciclovía as a socially inclusive program requires a dynamic measure of segregation that, in contrast to the traditional static and spatial segregation measures, recognizes and assesses segregation as a dynamic process that relies on individuals' daily life routines, as well as on the different ways they or social groups use urban space [43,44]. In this respect, a segregation measure should consider the dynamism of the "flow" of people in the Ciclovía rather than a place-based approach. ...
Ciclovía Recreativa is a program in which streets are closed off to automobiles so that people have a safe and inclusive space for recreation and for being physically active. The study aims were: (1) to compare participant’s spatial trajectories in four Ciclovía Recreativa programs in Latin America (Bogotá, Mexico City, Santiago de Cali, and Santiago de Chile) according to socioeconomic characteristics and urban segregation of these cities; and (2) to assess the relationship between participants’ physical activity (PA) levels and sociodemographic characteristics. We harmonized data of cross-sectional studies including 3282 adults collected between 2015 and 2019. We found the highest mobility for recreation in Bogotá, followed closely by Santiago de Cali. In these two cities, the maximum SES (socioeconomic status) percentile differences between the neighborhood of origin and the neighborhoods visited as part of the Ciclovía use were 33.58 (p-value < 0.001) and 30.38 (p-value < 0.001), respectively, indicating that in these two cities, participants were more likely to visit higher or lower SES neighborhoods than their average SES-of-neighborhood origin. By contrast, participants from Mexico City and Santiago de Chile were more likely to stay in geographic units similar to their average SES-of-origin, having lower overall mobility during leisure time: maximum SES percentile difference 1.55 (p-value < 0.001) and −0.91 (p-value 0.001), respectively. PA levels of participants did not differ by sex or SES. Our results suggest that Ciclovía can be a socially inclusive program in highly unequal and segregated urban environments, which provides a space for PA whilefacilitat physical proximity, exposure to new communities and environments, and interactions between different socioeconomic groups.
... Based on urban human interaction patterns, Shen (2019) successfully measured the extent to which two trajectories interacted with one another in daily activity spaces and captured the interaction potentials among various social groups. To recognize and assess segregation as a dynamic process, Li and Wang (2017) proposed a regression estimator that measured segregation by assessing the similarity between people and the social environments that they experienced in their daily activity spaces. ...
Research on the realistic and comprehensive identification of citywide spatial patterns of economic segregation is valuable for the sustainable development of cities. The consideration of human activities in segregation research inspires us to develop an alternative method to contribute to this type of research. In our method, we emphasize the combination of collective activity spaces (CASs) and spatial economic data, both of which are obtained from dynamic human activities. We first reveal the realistic use of urban spaces from human mobility patterns to generate multilevel CASs as basic analytical units. Then, we use a type of realistic economic data generated from human activities to measure the segregation level of each CAS. We realize this measurement by tailoring a segregation index, named the Term Frequency-Inverse Document Frequency-Index of Concentration at the Extremes-based (TFIDF-ICE-based) segregation index, for our economic data. Through these methods, we can uncover citywide multilevel spatial patterns of economic segregation realistically and comprehensively. Using Beijing and Wuhan as cases, we demonstrate and discuss the applicability and value of our method.
... High exposures to different social groups are considered as low levels of socio-spatial segregation and have a high potential for social interactions in this literature [30]. ...
An increasing number of studies have observed that ignoring individual exposures to non-residential environments in people’s daily life may result in misleading findings in research on environmental exposure. This issue was recognized as the neighborhood effect averaging problem (NEAP). This study examines ethnic segregation and exposure through the perspective of NEAP. Focusing on Xining, China, it compares the Hui ethnic minorities and the Han majorities. Using 2010 census data and activity diary data collected in 2013, the study found that NEAP exists when examining ethnic exposure. Respondents who live in highly mixed neighborhoods (with high exposures to the other ethnic group) experience lower activity-space exposures because they tend to conduct their daily activities in ethnically less mixed areas outside their home neighborhoods (which are more segregated). By contrast, respondents who live in highly segregated neighborhoods (with low exposures to the other ethnic group) tend to have higher exposures in their activity locations outside their home neighborhoods (which are less segregated). Therefore, taking into account individuals’ daily activities in non-residential contexts in the assessment of environmental exposure will likely lead to an overall tendency towards the mean exposure. Using Tobit models, we further found that specific types of activity places, especially workplaces and parks, contribute to NEAP. Ignoring individual exposures in people’s activity places will most likely result in misleading findings in the measurement of environmental exposure, including ethnic exposure.
... As has already been addressed in the preceding section, Kwan (2009;, for instance, supports the measurement of exposure to others at the individual level, especially in order to study segregation in activity spaces. Li and Wang (2017) and Wang et al. (2012) have measured activity space segregation in terms of the correlation between the social characteristics of individuals and those of their daily activity spaces. ...
Handbook of Urban Segregation
Sako Musterd (Ed.)
Abstract
The Handbook of Urban Segregation scrutinises key debates on spatial inequality in cities across the globe. It engages with multiple domains, including residential places, public spaces and the field of education. In addition, this comprehensive Handbook tackles crucial group-dimensions across race, class and culture as well as age groups, the urban rich, middle class, and gentrified households.
In a ‘world tour’ of urban contexts, the reader is guided through six continents confronting pressing segregation issues. Leading international scholars offer valuable insights across regional, ethnic, socioeconomic and welfare regime contexts. Three thematic parts explore key segregation questions worldwide, the multiple domains and dimensions of the topic and the methods, approaches and debates surrounding its measurement. Through these lenses, this timely Handbook provides a key contribution to understanding what urban segregation is about, why it has developed, what its consequences are and how it is measured, conceptualised and framed.
Containing clear use of visual aids alongside textual analysis, this Handbook will be an engaging and accessible resource for students and scholars with an interest in urban and human geography, cities and planning, and the wider field of urban studies.
March 2020 c 464 pp Hardback 978 1 78811 559 9
Research Handbooks in Urban Studies series
Contents:
INTRODUCTION
1. Urban segregation: contexts, domains, dimensions and approaches Sako Musterd
PART I Key Segregation Issues across the Globe; Urban Segregation in Cities in Africa, South America, Asia, Australia, Europe, and North America
2. Urban segregation in South Africa: the evolution of exclusion in Cape Town Jacobus van Rooyen and Charlotte Lemanski
3. Segregation by class and race in São Paulo Eduardo Marques and Danilo França
4. Residential segregation of rural migrants in post-reform urban China Zhigang Li and Feicui Gou
5. Dimensions of urban segregation at the end of the Australian dream Bill Randolph
6. Globalisation, immigration and ethnic diversity: the exceptional case of Vienna Josef Kohlbacher and Ursula Reeger
7. Do market forces reduce segregation? The controversies of post-socialist urban regions of Central and Eastern Europe Zoltan Kovacs
8. Urban and school segregation in the larger Paris metropolitan area: a complex interweaving with a strong qualitative impact on social cohesion Marco Oberti
9. Racial and economic segregation in the U.S.: overlapping and reinforcing dimensions Paul Jargowsky
PART II Multiple Domains and Dimensions of Segregation
10. Can the public space be a counterweight to social segregation? Ali Madanipour
11. Spatial segregation and the quality of the local environment in contemporary cities Roberta Cucca
12. Intersections of class, ethnicity and age: social segregation of children in the metropolitan region of Amsterdam Willem Boterman
13. Change and persistence in the third dimension: residential segregation by age and family type in Stockholm, 1990 and 2014 Åsa Bråmå and Roger Andersson
14. Segregation by household composition and income across multiple spatial scales Ann Owens
15. Middle-class family encounters and the role of neighbourhood settings and organisations for cross-social interaction Heike Hanhörster and Sabine Weck
16. Socioeconomic segregation and the middle classes in Paris, Rio de Janeiro and São Paulo: a comparative perspective Edmond Préteceille and Adalberto Cardoso
17. Segregation and the urban rich: enclaves, networks and mobilities Rowland Atkinson and Hang Kei Ho
18. The impact of gentrification on social and ethnic segregation Wouter van Gent and Cody Hochstenbach
19. Vertical social differentiation as segregation in spatial proximity Thomas Maloutas
20. Residential stratification and segmentation in the hyper-vertical city Ray Forrest, Ka Sik Tong and Weijia Wang
PART III Measuring and Conceptualising Segregation; Methods, Approaches, and Debates
21. Understanding the processes of changing segregation Nick Bailey
22. Integrating infrastructure and accessibility in measures of bespoke neighbourhoods John Östh and Umut Türk
23. On the meaning and measurement of the ghetto as a form of segregation Alan Walks
EPILOGUE
24. Towards further understanding of urban segregation Sako Musterd
Index
Edward Elgar monographs and handbooks are also available as ebooks at a paperback price on Google Play, ebooks.com and other ebook vendors. Our ebooks are published simultaneously with the print version and are typically priced at c £22.00/c $31.00 for a monograph.
... with the growing availability of new data sources and the advancement in computational powers. Conventional data sources for activity space research are travel diaries (Buliung & Kanaroglou, 2006;Farber, O'Kelly, Miller, & Neutens, 2015;Schönfelder & Axhausen, 2003;D. Wang et al., 2012;Wong & Shaw, 2011) and activity diaries (Jones & Pebley, 2014;F. Li & Wang, 2017;D. Wang & Li, 2016). Emerging data sources include GPS data (Raanan & Shoval, 2014), cell phone data (Järv, Müürisepp, Ahas, Derudder, & Witlox, 2015;Silm & Ahas, 2014;Yip, Forrest, & Xian, 2016), mobile phone apps (Palmer et al., 2013), and social media data (Huang & Wong, 2016;L. Li, Goodchild, & Xu, 2013;Q. Wang et al., 2018;Jiang et ...
It has become increasingly important in spatial equity studies to understand activity spaces-where people conduct regular out-of-home activities. Big data can advance the identification of activity spaces and the understanding of spatial equity. Using the Los Angeles metropolitan area for the case study, this paper employs geotagged Twitter data to delineate activity spaces with two spatial measures: first, the average distance between users' home location and activity locations; and second, the area covered between home and activity locations. The paper also finds significant relationship between the spatial measures of activity spaces and neighborhood spatial and socioeconomic characteristics. This research enriches the literature that aims to address spatial equity in activity spaces and demonstrates the applicability of big data in urban socio-spatial research.
... In this sense, we would argue that social psychologists might benefit from exploring methodological developments in companion disciplines. Such work includes innovations in the use of Participatory GIS methods for understanding how community members themselves perceive intergroup boundaries located across varying socio-spatial scales and across different social contexts (e.g., Huck et al., 2019), methods for estimating the global nature and extent of segregation of everyday activity spaces (e.g., Li & Wang, 2016), and methods for tracking and analysing individuals' everyday movements in cities (e.g., Greenberg Raanan & Shoval, 2014). ...
Social psychological research has increasingly extolled the benefits of intergroup contact as a means of promoting positive relations. However, a growing body of research suggests that formal policies of desegregation are often offset by informal ‘micro-ecological’ practices of (re)-segregation, in everyday life spaces. This paper presents a systematic literature review of recent evidence on this topic (2001-2017), outlining key findings about how, when, where, and why micro-ecological divisions are reproduced. Informal segregation can happen based on ethnicity, religion, socioeconomic status, gender, or gender and ethnicity, despite people being in a shared place. People generally maintain patterns of ingroup isolation as a result of: a) negative attitudes and stereotypes; b) ingroup identification and threat; or c) feelings of anxiety, fear and insecurity. Educational settings have been the main context studied, followed by leisure and recreational places, public urban places and public transport. The paper also identifies three areas of potential future research, highlighting the need to: (1) capitalise on methodological innovations; (2) explore systematically how, when and why the intersectionality of social categories may shape micro-ecological practices of contact and separation; and (3) understand more fully why micro-ecological patterns of segregation are apparently so persistent, as well as how they might be reduced.
... Four visual methods, including space-time paths, time windows, density surface and ring-graphs of social networks, were proposed to analyse socio-spatial isolation patterns in a three-dimensional space (Lee and Kwan 2011). Moreover, efforts included the incorporation of actual spatial behaviours with mental maps (Rannan and Shoval 2014), the visualisation of the concentration of daily activities for specific population groups (Wang et al. 2012), the development of new methods measuring social interaction potential as the size of the intersected part between the space-time prisms derived from people's mobility distributions (Farber et al. 2014;, the utilisation of a regression estimator to assess the similarity of individuals' experienced segregation in their daily activity spaces (Li and Wang 2017), and the mapping of the hourly changing segregation experienced by citizens of various educational or socio-professional backgrounds in Paris (Roux et al. 2017). With the emerging data about people's movement, recent attention has been directed towards describing the spatiotemporal variation of segregation measurements. ...
People are socially divided through urban space, where they experience segregation dynamically. By conceptualising mobilised social inclusion as the gravitational interactions between urban human flow patterns, this article introduces a framework for measuring the extent to which two trajectories interact with one another in daily activity space, in which a series of indices, theoretically equivalent to those developed in segregation research, are produced to capture the interaction potentials among various social groups from different perspectives. These scopes include absolute, relative and multi-group using pairs of places as analysis units, as well as place-based measurements that are very sensitive to the spatial configuration of the flow-based spatial interaction potentials. The application in the case of Greater London finds that the relative indices capture the spatial differentiation among various modes of interactions, portraying the between-domains exposure levels might be experienced by different occupations when they commute across places. The study demonstrates that mobilised interaction is influenced by between-domains mobility, and the proposed approach can provide a network understanding of social exposure through the edges between every two place nodes, going beyond existing place-based measurements. In addition, the changes between place-based results aggregated by origins and those determined by destinations showcase the dynamic shift of in-site exposure during peak hours. Though only commuting behaviours are demonstrated in this work, the framework introduced can be easily extended to the spatial interactions between any flow trajectories for any spatial unit, e.g., place (point-wise), place pairs (pair-wise), or specified routes (path-wise), within the activity space defined by time geography or the life-course domain approach.
... Second, besides activity-travel time budget, it is worthwhile to include monetary cost budget in the model framework because much of the daily travel and activity participation involve monetary expenses. While it is not to claim that travel does not, activity participation cost is often the key determinant of accessibility for certain social groups (Li and Wang 2017). With the presence of cost budget, dual frontiers are required to be searched. ...
Space–time prism (STP), which envelops the spatial and temporal opportunities for travel and activity participation within a time frame, is a fundamental concept in time geography. Despite many variants, STPs have been mostly modeled for one flexible activity between two anchor points. This study proposes a systemic approach to construct the STP bounds of activity programs that usually include various possible realizations of activity chains. To that effect, multi-state supernetworks are applied to represent the relevant path sets of multi-activity travel patterns. A goal-directed search method in multi-state supernetworks is developed to delineate the potential space–time path areas satisfying the space–time constraints. Particularly, the approximate lower and upper STP bounds are obtained by manipulating the goal-directed search procedure utilizing landmark-based triangular inequalities and spatial characteristics. The suggested approach can in an efficient fashion find the activity state dependent bounds of STP and potential path area. The formalism of goal-directed search through multi-state supernetworks addresses the fundamental shift from constructing STPs for single flexible activities to activity programs of flexible activity chains.
... Even in using alternative definitions, Coulton et al. (2001) find that the correlations of values between these newly defined neighborhoods and censustract definitions for percent poverty and percent female-headed families exceed 0.80, suggesting that variation in substantive characteristics based upon alternative neighborhood definitions is quite similar to that found among census tracts. More recently, researchers have found that individuals spend a substantial amount of time away from their homes in "activity spaces"; however, it has been shown that they experience substantial levels of segregation in those places, sometimes as much as they experience in their home environment (e.g., Jones and Pebley 2014;Li and Wang 2017). In sum, census-tracts should remain an important proxy for neighborhoods moving forward in urban quantitative research, and particularly research that focuses on residential inequality. ...
The Hong Kong government adopts laissez-faire policies on urban renewal, suburbanization, education, and industrial relations, while actively intervening in the housing markets; it limits the land supply for housing, excluding the majority of the population from applying for public housing and adopting a rail-plus-property model to boost land prices. Transport studies usually use socioeconomic variables to predict travel demand, but this study uses the self-organization approach to explain the commuting patterns produced by the interaction between policies and individual actions. Drawing on data from the 2011 Hong Kong TCS, this study finds that the policies contribute to causing poverty, social and spatial segregation, and accessibility constraints of poor workers. Accessibility problems produce an imbalanced social environment. Since people want fair governance, implementing fair governance is the main motivation for the Hong Kong government to amend the policies to return the social system to a balanced state and improve the commuting of low-income workers.
Activity-space segregation is a new topic in urban segregation studies. The existing literature did not fully explain its mechanisms. In this study, we tested the hypothesis whether activity-space segregation is influenced by individual daily activity patterns. The dynamic ambient population that individuals interact with was identified with mobile phone big data and the individuals’ experienced segregation at the activity space was measured with travel survey small data. This study compared the differences in individuals’ daily activity patterns by different social-economic groups and examined the influence of the spatiotemporal pattern on the activity-space segregation. It has found that: (1) The degree of segregation in non-mandatory activity spaces during the day is less than that in mandatory activity spaces, and the degree of segregation in residence is the highest. (2) Disadvantaged populations have less leisure time and smaller activities space, and they are subject to greater time and space constraints. Spatiotemporal patterns affect the degree of an individual’s segregation. Those who have less leisure time and shorter travel distances experience higher activity-space segregation. This study has both theoretical and practical significance. The social interaction in non-mandatory activities is very important for decreasing activity-space segregation. It facilitates scientific decision-making for the government.
This work presents a novel approach to studying ethnic segregation from the perspective of linguistic landscapes. Numerous street-level images accumulated over the last two decades have enabled the exploration of linguistic landscapes at a larger scale than ever before. Since the prevalence of a specific language in a public space implies the linguistic group inhabiting the area, its careful evaluation can reveal the degree of segregation between linguistically different ethnic groups. To demonstrate the effectiveness of the proposed approach, we applied it to the linguistic landscape of Seoul, South Korea. Using a large set of street-level images collected from an online map platform, we measured the levels of segregation between Korean and Chinese signs from 2010 to 2018. The levels of segregation on the street-level images were different to a certain extent from those of residential segregation. While residential segregation gradually increased between 2010 and 2018, except for 2011, more fluctuations were observed in linguistic segregation. This finding is likely because a linguistic landscape is shaped mainly by advertising signs, banners, and billboards in commercial areas, and such commodified urban spaces change more dynamically to attract inhabitants and visitors. These results suggest that the proposed approach can offer an alternative way of understanding the complex socio-demographic phenomenon from a new perspective, as with other emerging data sources in the era of big data.
Housing reform in socialist China has incurred considerable restructuring and transformation of urban space and society. Yet its specific socio-spatial outcomes have not been fully investigated from the perspective of housing type at the meso- and micro-levels. This study attempts to fill the gap by examining the nature and magnitude of the consequences of housing reform and the corresponding effects on mobility. Specifically, based on census data and a mobility survey, this paper combines statistical breakdowns and structural equation modeling to capture the socio-spatial differentiation of urban structure resulting from housing reform and its influences on individual vehicle kilometers traveled (VKT) and transportation walking. The results reveal that: (1) different types of housing tend to feature internally homogeneous populations in terms of socio-economic composition and socio-psychological condition, with pronounced social stratification; (2) residents in different types of housing display dramatically different travel styles, with substantial mobility inequities; (3) social differentiation appears to have spatial determinants; in particular spatial segregation contributes to increasing social exclusion; (4) the effects of spatial and social characteristics on mobility are led by housing type; and (5) individual mobility patterns are shaped by the joint influences of spatial and social dimensions of housing differentiation. The findings contribute to further understanding of socio-spatial differentiation in countries with a transitional housing market, suggesting that the design of land-use policies should recognize their social effects and that urban mobility planning practices should deliver sustainability that serves a diverse population, including in particular disadvantaged groups in public and replacement housing. This study serves as a mirror to observe the urban transition compared to other political economies and adds additional richness and diversity to the theoretical debates on the issue of socio-spatial differentiation and empirical evidence on residential and mobility inequities across global contexts.
Das Wohnen in der Stadt ist für die meisten Menschen in Deutschland zur Normalität geworden. Mit der fortgeschrittenen Urbanisierung ist deshalb die Frage nach der Bedeutung des Städtischen umso wichtiger geworden. Die Definition des „urbanen“ Zusammenlebens hat dabei im Laufe der intellektuellen Auseinandersetzung mit dem Wohnen in der Stadt unterschiedliche Vorstellungen hervorgebracht, die heute durch die Globalisierung und Virtualisierung der Stadt wiederum neu betrachtet werden müssen. Das Wohnen in der Stadt wurde seit den ersten Studien in der Stadtsoziologie immer auch als Spiegelbild für die sozialen Ungleichheiten in der Gesellschaft gesehen. Mit dem Begriff der residentiellen Segregation wird seitdem vor allem thematisiert, in welcher Distanz und Nähe zu anderen sozialen Gruppen Menschen in der Stadt wohnen. Sozialen Entfernungen werden zunehmend anhand von Prozessen der Gentrifizierung neu vermessen und geben Aufschluss über grundlegende Veränderungen in der Gesellschaft. In diesem Kapitel wird eine Übersicht über die Entwicklung der Diskurse zum Thema Urbanität, Segregation und Gentrifizierung gegeben und diese in den aktuellen Stand des Wohnens in deutschen Städten eingeordnet.
It is established that socioeconomic and demographic dissimilarities between populations are determinants of spatial segregation. However, the understanding of how such dissimilarities translate into actual segregation is limited. We propose a novel network-analysis approach to comprehensively study the determinants of communicative and mobility-related spatial segregation, using geo-tagged Twitter data. We constructed weighted spatial networks representing tie strength between geographical areas, then mod-eled tie formation as a function of socioeconomic and demographic dissimilarity between areas. Physical and virtual tie formation were affected by income, age, and race differences , although these effects were smaller by an order of magnitude than the geographical distance effect. Tie formation was more frequent when "destination" area had higher median income and lower median age. We hypothesize that physical tie formation is more "costly" than a virtual one, resulting in stronger segregation in the physical world. Economic and cultural motives may result in stronger segregation of relatively rich and young populations from their surroundings. Our methodology can help identify types of states that lead to spatial segregation, and thus guide planning decisions for reducing its adverse effects.
Over the last decade, socio‐spatial segregation of different population groups in activity spaces has attracted considerable attention from geographers and sociologists. A careful examination of such activity‐space segregation can provide a more comprehensive description of dynamic intergroup relations in society than conventional residential segregation, as its measurement is not limited to a particular place. However, the evaluation of activity‐space segregation is a challenging task because it requires the manipulation of large spatiotemporal data sets. The lack of software tools that can assist researchers in exploring and analyzing individuals’ travel trajectories is one of the significant impediments to empirical studies. To address this practical limitation, this article proposes a combined use of two R packages, namely, slice and seg: the former provides tools for storing and manipulating activity‐space data, and the latter implements several existing measures of segregation that can be applied to various social places. The proposed approach is applied to the measurement of activity‐space segregation between high‐ and low‐income groups in Seoul to illustrate the key features of the packages. The results demonstrate that the use of these packages would make it more convenient to conduct activity‐space segregation research and enhance our understanding of the complex socio‐spatial phenomenon.
This article examines macro‐level contextual parameters and individual‐based factors deemed in the literature to directly influence individuals’ daily mobility practices. It considers the urban structure, place of residence, situation in the social hierarchy, and position in the life course. Taking its inspiration from approaches highlighting segregation at destination place and studies focusing on mobility biographies, it enquires whether systematic discrepancies may be detected between the places frequented for work or study on a daily basis by the groups of individuals residing in the same neighborhood. It also looks at whether home location (in a central area, inner suburb, or outskirt) influences how action spaces are configured. The analysis relies on a three‐phase integrated method. First, a typology of individuals is assembled to put together homogenous socio‐demographic groups. Second, the action spaces of these groups are calculated and mapped. Third, the significance of spatial differences in action spaces is assessed using a bivariate colocation test, hitherto used primarily in spatial epidemiology. This three‐phase method is applied to data collected in Santiago de Chile during a survey of 1,000 households, designed to capture spatial mobility from a biographical perspective.
L’étude a pour finalité de comprendre l’évolution des conditions d’accès des individus à leurs lieux d’activité dans un contexte de transformations urbaines et de changements sociodémographiques. Nous cherchons en particulier à savoir si aujourd’hui les adultes pâtissent d'une localisation résidentielle plus favorable à leurs enfants du point de vue des mobilités quotidiennes ou si d’autres logiques sont à l’œuvre entre autres suivant la position des ménages dans la hiérarchie sociale. Nous cherchons également à comprendre comment ont évolué les conditions de mobilité suivant le lieu de résidence dans la ville. Pour ce faire, nous définissons un nouveau concept appelé "espace-temps d’action" dont nous proposons une représentation cartographique. Nous étudions à l’aide des "espaces-temps d’action" l’évolution conjointe, entre 1993 et 2009, de l’accessibilité aux lieux d’étude et de travail des adultes actifs et des enfants corésidents au sein de ménages enquêtés à Bogotá (Colombie). La méthode d’élaboration des espaces-temps d’action est détaillée, ses avantages et limites sont discutés.
Studies on ethnic segregation in recent decades have shifted the focus from the residential area towards people's activity locations, and policy focus requires more effort in identifying the most segregated individuals among ethnic minorities. Meanwhile, research on ethnic segregation in urban China from the activity‐based perspective remains to be examined. Thus, we analyse ethnic segregation beyond residential neighbourhoods in Chinese cities by measuring individuals' exposure to the social context in neighbourhoods where they perform daily activities (e.g., work). We conduct an empirical study in Xining City based on activity diaries with the focus on the Hui minorities and Han majorities. We find that a substantial amount of activities occur outside the respondents' home neighbourhoods, thereby resulting in notable ethnic segregation beyond home neighbourhoods. In addition, using one‐sided exposure indices and multilevel regression models, we find that different social groups may be exposed to considerably different ethnic environments in activity locations even if they live in ethnically similar neighbourhoods and identify that individuals with low income or low‐level education among the Hui minorities may have greater risks of segregation in activity locations than others. Therefore, the gaps in income and educational attainment between the Hui minorities and Han majorities should be mitigated to foster high opportunities of encounters and social integration.
The study of segregation is essential for understanding how place influences life outcomes. Traditional segregation indices rely heavily on the use of areal units for calculation, which risks introducing both measurement and interpretation error. Using individual-level data avoids many of the problems facing traditional area-level indices. However, few segregation indices currently exist that are capable of utilizing such data. Given that our understanding is only as good as our measurement, it is imperative that our measures accurately reflect our perceptions of segregation. Utilizing the recent release of the complete 1940 Census count data, this article details a new individual-level segregation measure—the shortest path isolation (SPI) index. The SPI index captures the degree of racial isolation experienced by an individual, regarding both distance and interpersonal contact. With West Philadelphia as a sample study area, this article highlights the benefits of the SPI index for studying segregation at the individual level.
There is now an extensive literature demonstrating that experiences of migration and diversity differ significantly between and across local geographies. Three broad explanations for differences in local outcomes have been put forward (Robinson, 2010): first, population composition – the characteristics of individuals living in the neighbourhood; second, context – the social and physical environment; and third, community – socio-cultural histories and collective identities. Few studies examine the linkages between all three explanations and their relative importance. This article applies all three explanations to intergroup relations in a super-diverse context. It draws on data from a mixed methods case study of a neighbourhood in Glasgow, Scotland where long-term white and ethnic minority communities reside alongside Central and Eastern European migrants, refugees and other recent arrivals. The evidence comprises local statistics and documentary evidence, participant observation and qualitative and walk-along interviews with residents and local organisations. The findings highlight the different ways in which people respond to super-diversity, and the importance of the neighbourhood context and the material conditions for intergroup relations. The article thus demonstrates the ambiguities that arise from applying the dynamics of population composition, context and community to neighbourhood analysis, with implications for the study of neighbourhoods more widely.
Given ongoing developments altering social and spatial cohesion in urban societies, a more comprehensive
understanding of segregation is needed. Taking the ‘mobilities turn’ at heart, we move
beyond place-based segregation approaches and focus on the practised urban experiences of individuals
through a more comprehensive assessment of their activity spaces. This study contributes
to people-based segregation research by mapping the activity spaces of individuals on the basis of
mobile phone data in Tallinn (Estonia) and relating these activity spaces to (mainly) the users’ ethnic
background (i.e. Estonian versus Russian). Significant ethnic differences in terms of (1) the
number of activity locations, (2) the geographical distribution of these locations, and (3) the overall
spatial extent of activity spaces are found. We also find that these differences tend to deepen
as the temporal framework is extended. We discuss the main implications for segregation
research and highlight some avenues for further research.
In this article, we extend research on neighborhood social isolation by (1) examining residents of disadvantaged and advantaged communities and (2) considering the character of neighborhoods where people conduct routine activities away from home. We contend that social isolation is experienced by residents of both highly disadvantaged and highly advantaged neighborhoods because the two groups spend time in largely nonoverlapping parts of the city. Individual and neighborhood race-ethnic dynamics exacerbate such social isolation. Data from the Los Angeles Family and Neighborhood Survey show that social isolation is experienced by residents of all areas of the city, whether highly disadvantaged or advantaged. African Americans, Latinos and residents of areas with many Latinos suffer additional penalties in the social isolation of disadvantage in where they conduct routine activities.
Anecdotal evidence suggests that â–˜gated communitiesâ–™ are growing in popularity. This paper uses empirical evidence to profile the location and characteristics of gated development in England and details the relative integration of residents. The paper also attempts to think through the wider theoretical and urban policy impacts of gating. In contrast to the view that gated communities provide an extreme example of residential segregation we go further and argue that the timeâ–space trajectories of residents suggest a dynamic pattern of separation that goes beyond the place of residence. Gated communities appear to provide an extreme example of more common attempts by other social groups to insulate against perceived risk and unwanted encounters. Patterns of what we term timeâ–space trajectories of segregation can thereby be seen as closed linkages between key fields, such as work and home, which enable social distance to be maintained and perceived risks to be managed by elite social groups. We conclude that gated communities further extend contemporary segregatory tendencies in the city and that policy responses are required which curtail the creation of such havens of social withdrawal.
Many fundamental notions in geographic and social science research still tend to be conceptualized largely in static spatial terms, ignoring how our understanding of the issues we study can be greatly enriched through the lenses of time and human mobility. This article revisits three such notions: racial segregation, environmental exposure, and accessibility. It argues for the need to expand our analytical focus from static residential spaces to other relevant places and times in people's everyday lives. Mobility is an essential element of people's spatiotemporal experiences, and these complex experiences cannot be fully understood by just looking at where people live. As many social scientists are interested in studying segregation, environmental exposure, and accessibility, geographers can contribute to advancing temporally integrated analysis of these issues through careful examination of people's everyday experiences as their lives unfold in space and time. Interdisciplinary research along this line could have a broad impact on many disciplines beyond geography.
Research on neighborhood effects has focused largely on residential neighborhoods, but people are exposed to many other places in the course of their daily lives-at school, at work, when shopping, and so on. Thus, studies of residential neighborhoods consider only a subset of the social-spatial environment affecting individuals. In this article, we examine the characteristics of adults' "activity spaces"-spaces defined by locations that individuals visit regularly-in Los Angeles County, California. Using geographic information system (GIS) methods, we define activity spaces in two ways and estimate their socioeconomic characteristics. Our research has two goals. First, we determine whether residential neighborhoods represent the social conditions to which adults are exposed in the course of their regular activities. Second, we evaluate whether particular groups are exposed to a broader or narrower range of social contexts in the course of their daily activities. We find that activity spaces are substantially more heterogeneous in terms of key social characteristics, compared to residential neighborhoods. However, the characteristics of both home neighborhoods and activity spaces are closely associated with individual characteristics. Our results suggest that most people experience substantial segregation across the range of spaces in their daily lives, not just at home.
We put forward a method for measuring the social interaction potential of a metropolitan region based on the time-geographic concept of joint accessibility. The metric is sensitive to prevailing land use patterns and commuter flows in the metropolitan region, time budgets, and the spatial distribution of joint activity locations. It is calculated via a geocomputation routine in which a representative subset of after-work, space–time prisms are intersected with each other. Decomposition of the metric gives rise to social potential metrics for each employment and residential zone in the city, for specific commuter flows, and for locations of potential social interaction, such as bars, restaurants, sports fields, and so on. The method is demonstrated via a scenario-based experiment that explores the impact of residential and employment land use patterns and varying levels of commuter flow dispersion. The findings indicate that the metric is adequately responsive to each of the scenario input parameters, as well as pairwise combinations of parameters. Following the experiment, an empirical example using flow data from Salt Lake City, Utah, is presented. Insights on how to introduce more realism in the calculation of the metric for actual metropolitan regions for comparative purposes are then put forward. Finally, the article concludes with a discussion of the broader applications of this metric to various topical areas in urban geography including segregation, social capital development, innovation and creativity, and location allocation of facilities and their opening hours.
This article explores new methods for gathering and analyzing spatially rich demographic data using mobile phones. It describes a pilot study (the Human Mobility Project) in which volunteers around the world were successfully recruited to share GPS and cellular tower information on their trajectories and respond to dynamic, location-based surveys using an open-source Android application. The pilot study illustrates the great potential of mobile phone methodology for moving spatial measures beyond residential census units and investigating a range of important social phenomena, including the heterogeneity of activity spaces, the dynamic nature of spatial segregation, and the contextual dependence of subjective well-being.
This research seeks to contribute to advancing qualitative methodologies at the intersection of qualitative geographic information systems (GIS), narrative analysis, 3D GIS-based time-geographic methods, and computer-aided qualitative data analysis. The approach to GIS-based narrative analysis developed in the study, called "geo-narrative," is based on extending current GIS capabilities for the analysis and interpretation of narrative materials such as oral histories, life histories, and biographies. The three central elements in this approach are (1) narrative analysis as the qualitative approach; (2) 3D GIS-based time-geographic methods as the representational framework; and (3) 3D-VQGIS as the GIS-based computer-aided qualitative data analysis component. A case example based on a study of the lives of the Muslim women in Columbus, Ohio, after 11 September 2001 is used to illustrate the approach.
When scholars map the urban geography of racial and ethnic segregation, they privilege the time when people are at home. When workers commute, however, the tract of residence of one group often becomes the tract of employment of others. It follows that an exclusive focus on the residential geographies of racial groups erases the presence of others who work in those neighborhoods. Not only does this analytical orientation create a false impression of a city's racialized spaces as fixed, but it also misleadingly characterizes neighborhoods as the domain of those who live, rather than work, in them. In addressing this oversight, the study compares levels of residential and work tract segregation for native-born and immigrant groups in a large U.S. metropolitan area, Los Angeles. The analysis reveals that segregation by work tract is considerably lower than by residential tract, suggesting more intergroup interaction takes place during working hours than at home. The difference in segregation between residence and work is very large in the case of native-born whites and Mexican immigrants. These two groups maintain substantially different residential geographies but are quite likely to work in the same tracts. Such work tract complementarities are gender sensitive; they are much more likely between native-born white and Mexican men than between women of these groups. This gendered difference holds across all groups, with men more likely to work in tracts with men from other groups than women with women from other groups. The study offers new perspective on diurnal shifts in urban racial segregation. We conclude by speculating that reduced segregation at workplaces factors into recent increases in rates of interracial partnering, which may, in turn, ultimately leverage change in residential segregation.
This article suggests that common segregation indices be complemented by an additional index that emphasizes an agent’s isolation from members of other groups in everyday life. We suggest a “sociospatial isolation” index that is studied for individual agents in respect to the spaces in which they actually conduct their everyday life, leading to the aggregation of individuals who share the same orientation toward segregation. The index refers to both the territorial and the interactive contexts of seven aspects: home vicinity, cluster of neighboring homes, neighborhood and city in the territorial context, and friends, work, and leisure activities in the interactive context of an agent’s everyday life activity spaces. The article calculates some hypothetical examples that demonstrate the qualities of the index and is followed by the case of African migrant workers and their segregation in one neighborhood of the inner city of Tel Aviv. The index may receive values between 0–1, with the value of 0.5 representing population mixture, lower values representing exposure to members of other groups, and higher values representing an agent’s tendency toward isolation from members of alternative groups. The results emphasize the fact that there was no correlation between the territorial and the interactive dimension of sociospatial isolation. African migrant workers maintained extremely high rates of intergroup isolation, regardless of their territorial isolation.
The undesirable effect of public housing on poverty concentration has been recognised by a series of studies that use census-tract-level aggregate data. This paper examines whether the poverty concentration mechanism of public housing that has been observed elsewhere also functions in Hong Kong. Hong Kong has one of the largest supplies of public housing in the world and also a distinct urban environment. After assessing the poverty rates in Hong Kong in 1991 and 2001, we build a series of regression models to examine the impact of public rental housing on poverty concentration during the 1990s. Using aggregated census tract-level data, the analysis concludes that public housing does not necessarily concentrate poverty in particular census tracts. Public policy and city planning by the Hong Kong government are found to be effective in avoiding or reducing the possible adverse effect of public housing by maintaining social heterogeneity and spatial homogeneity.
While the literature clearly acknowledges that individuals may experience different levels of segregation across their various socio-geographical spaces, most measures of segregation are intended to be used in the residential space. Using spatially aggregated data to evaluate segregation in the residential space has been the norm and thus individual's segregation experiences in other socio-geographical spaces are often de-emphasized or ignored. This paper attempts to provide a more comprehensive approach in evaluating segregation beyond the residential space. The entire activity spaces of individuals are taken into account with individuals serving as the building blocks of the analysis. The measurement principle is based upon the exposure dimension of segregation. The proposed measure reflects the exposure of individuals of a referenced group in a neighborhood to the populations of other groups that are found within the activity spaces of individuals in the referenced group. Using the travel diary data collected from the tri-county area in southeast Florida and the imputed racial-ethnic data, this paper demonstrates how the proposed segregation measurement approach goes beyond just measuring population distribution patterns in the residential space and can provide a more comprehensive evaluation of segregation by considering various socio-geographical spaces.
"Activity space" has been used to examine how people's habitual movements interact with their environment, and can be used to examine accessibility to healthcare opportunities. Traditionally, the standard deviational ellipse (SDE), a Euclidean measure, has been used to represent activity space. We describe the construction and application of the SDE at one and two standard deviations, and three additional network-based measures of activity space using common tools in GIS: the road network buffer (RNB), the 30-minute standard travel time polygon (STT), and the relative travel time polygon (RTT). We compare the theoretical and methodological assumptions of each measure, and evaluate the measures by examining access to primary care services, using data from western North Carolina.
Individual accessibility is defined as the availability of healthcare opportunities within that individual's activity space. Access is influenced by the shape and area of an individual's activity space, the spatial distribution of opportunities, and by the spatial structures that constrain and direct movement through space; the shape and area of the activity space is partly a product of how it is conceptualized and measured. Network-derived measures improve upon the SDE by incorporating the spatial structures (roads) that channel movement. The area of the STT is primarily influenced by the location of a respondent's residence within the road network hierarchy, with residents living near primary roads having the largest activity spaces. The RNB was most descriptive of actual opportunities and can be used to examine bypassing. The area of the RTT had the strongest correlation with a healthcare destination being located inside the activity space.
The availability of geospatial technologies and data create multiple options for representing and operationalizing the construct of activity space. Each approach has its strengths and limitations, and presents a different view of accessibility. While the choice of method ultimately lies in the research question, interpretation of results must consider the interrelated issues of method, representation, and application. Triangulation aids this interpretation and provides a more complete and nuanced understanding of accessibility.
Increased mobility has posed a challenge to the study of social segregation which conventionally adapts a static view in linking people's source of identity and social interaction to confined spaces of their residence. This is a paper reporting an exploratory study in the use of a mobile phone app in tracking the mobility patterns of selected sample of people in Hong Kong. It explores the impacts of mobility on whom people engage their activities with, how they interact with people in their home neighbourhood and how much likely they would in interacting with people of different socio-economic backgrounds. Patterns of mobility are very uneven among people in Hong Kong and as a city of long working hours and heavy work burden, the time people stay in their home neighbourhood and interaction with friends are in fact very limited. There are also high opportunities for them to move to neighbourhoods with a different socio-economic profile with that they live in. Yet people from poor neighbourhoods tend to move to poor neighbourhoods whilst richer people to richer neighbourhoods. Thus pole may be mobile but interaction with other income groups may be limited. At the same time, the mobile phone app that has been developed offers a very robust instrument for social research which needs to track people's movement
Individual activity patterns are influenced by a wide variety of factors. The more important ones include socioeconomic status (SES) and urban spatial structure. While most previous studies relied heavily on the expensive travel-diary type data, the feasibility of using social media data to support activity pattern analysis has not been evaluated. Despite the various appealing aspects of social media data, including low acquisition cost and relatively wide geographical and international coverage, these data also have many limitations, including the lack of background information of users, such as home locations and SES. A major objective of this study is to explore the extent that Twitter data can be used to support activity pattern analysis. We introduce an approach to determine users’ home and work locations in order to examine the activity patterns of individuals. To infer the SES of individuals, we incorporate the American Community Survey (ACS) data. Using Twitter data for Washington, DC, we analyzed the activity patterns of Twitter users with different SESs. The study clearly demonstrates that while SES is highly important, the urban spatial structure, particularly where jobs are mainly found and the geographical layout of the region, plays a critical role in affecting the variation in activity patterns between users from different communities.
This work investigated the effect of personal, household, and neighborhood characteristics on variations in activity space through the use of a shortest network path buffer approach. A special focus was on the comparison of older adults (age 65 years and older, sample size of 591) with working-age adults (age 25 to 59 years, sample size of 1,806) to understand better the changes in activity space with age. Because activity space was a limited measure of social activity dependent on assumptions, this work investigated relative differences in the geographic reach of activity space and factors that increased or decreased that reach. The data were from the 2006 Household Activity Survey conducted in the Puget Sound, Washington, region. Descriptive data analysis showed that older adults on average had a substantially smaller (23%) geographic reach of activity space compared with working-age adults and that older adults who did not drive had the smallest geographic reach of activity space, only 16.7% of the overall average. The regression model results showed that low household income, often correlated with reduced mobility, was associated with a reduced geographic reach of activity space for older adults. Activity frequency significantly increased the geographic reach of activity space, and the effect was larger for older adults. The geographic reach of activity space was associated with neighborhood characteristics. Living in suburban and exurban neighborhoods led to a larger geographic reach of activity space for both older and working-age adults, while living in mixed use neighborhoods led to a smaller geographic reach of activity space.
Differences in individuals' exposure to social/physical environment in daily life or activity–space segregation have aroused renewed interests in socio-spatial separation in geography and urban studies. However, there are not many empirical studies that comprehensively assess activity–space segregation perhaps due to the scarcity of detailed data to define and characterize activity space. This paper aims to help fill in this gap by contributing an empirical study in Hong Kong. We compare the daily life experiences of public and private housing residents in terms of activity space and exposure to people in their daily life. We find that inhabitants of public housing in Hong Kong are disadvantaged in many ways. Public housing residents' lower socio-economic status, smaller homes, and lower car ownership distinguish them from inhabitants of private housing. We also find that the activity spaces of these residents are not necessarily smaller than those of private housing residents. Public housing residents in fact have more extensive activity spaces and spend more time out of the home. However, their activity spaces are socio-economically different from those of private housing residents. They are more likely exposed to people similar to themselves than private housing residents. This study offers some important empirical evidence on activity–space segregation as well as improves the understanding about socio-spatial distance between public and private housing residents of Hong Kong.
The index of dissimilarity is complemented with a second index. The resulting ability to measure spatial isolation as well as spatial dissimilarity adds significantly to the techniques available for describing and summarising segregation.-from Editors
In this paper we discuss how cyberspace has been interwoven in the geographies of social stratification and segregation nowadays. It conceptualizes `virtual segregation' as an extension of the `digital divide' and sociospatial segregation in urban spaces. A case study was conducted in Hong Kong in 2010, when 770 Internet users were surveyed. A comparison of their patterns of Internet use shows that these individuals, all of whom possess devices and Internet access, have varied levels of connectivity in cyberspace. A typology of Internet users was then derived from the perspective of virtual segregation. The findings suggest that people may be stratified and segregated in cyberspace in similar ways to the physical world, and that segregation studies should pay more attention to virtual segregation.
This paper conceives of residential segregation as a multidimensional phenomenon varying along five distinct axes of measurement:
evenness, exposure, concentration, centralization, and clustering. Twenty indices of segregation are surveyed and related
conceptually to one of the five dimensions. Using data from a large set of U.S. metropolitan areas, the indices are intercorrelated
and factor analyzed. Orthogonal and oblique rotations produce pattern matrices consistent with the postulated dimensional
structure. Based on the factor analyses and other information, one index was chosen to represent each of the five dimensions,
and these selections were confirmed with a principal components analysis. The paper recommends adopting these indices as standard
indicators in future studies of segregation.
The widespread development of gated communities has generated much concern over urban fragmentation and social segregation. The social division and segregation between residents inside and outside urban enclaves exist not only in their residential spaces, but also in their values, social relations, and daily lives. In this study, it is argued that sociospatial segregation research should pay more attention to individuals' actual usage of urban space in their daily lives. By examining the activity space of the residents from different types of neighborhoods, a spatiotemporal approach to studying sociospatial segregation in Beijing, China is described. Significant differences are found in the usage of time and space between residents inside and outside the so-called privileged enclaves. Their activity spaces are found to vary significantly in terms of extensity, intensity, and exclusivity. The study suggests that the fragmentation of urban space is the result not only of residential segregation, but also of how different social groups spend their time and use urban space.
Based on concomitant time-series analyses, the results of this study support distinct social interaction correlates for the mood dimensions of negative affect (NA) and positive affect (PA). Participants (N = 25) completed structured diaries three times daily for 4 weeks assessing their PA, NA, and participation in five types of social interaction. A significant number of participants' data series evidenced significant positive correlations between PA and fun/active and necessary/informational types of social interaction, and between NA and arguing/confronting and receiving help/support, during synchronous diary periods. Providing help/support was not related to NA or PA. No hypothesized time-lagged relations between mood and social interaction variables were present suggesting that, if these associations exist, they may be at intervals shorter than the one third day recording frequency used in this study. Results are discussed in the context of research on mood, social interaction, and time-series analysis.
Current knowledge about the relationship between transport disadvantage and activity space size is limited to urban areas, and as a result, very little is known about this link in a rural context. In addition, although research has identified transport disadvantaged groups based on their size of activity space, these studies have, however, not empirically explained such differences and the result is often a poor identification of the problems facing disadvantaged groups. Research has shown that transport disadvantage varies over time. The static nature of analysis using the activity space concept in previous research studies has lacked the ability to identify transport disadvantage in time. Activity space is a dynamic concept; and therefore possesses a great potential in capturing temporal variations in behaviour and access opportunities. This research derives measures of the size and fullness of activity spaces for 157 individuals for weekdays, weekends, and for a week using weekly activity–travel diary data from three case study areas located in rural Northern Ireland. Four focus groups were also conducted in order to triangulate quantitative findings and to explain the differences between different socio-spatial groups. The findings of this research show that despite having a smaller sized activity space, individuals were not disadvantaged because they were able to access their required activities locally. Car-ownership was found to be an important life line in rural areas. Temporal disaggregation of the data reveals that this is true only on weekends due to a lack of public transport services. In addition, despite activity spaces being at a similar size, the fullness of activity spaces of low-income individuals was found to be significantly lower compared to their high-income counterparts. Focus group data shows that financial constraint, poor connections both between public transport services and between transport routes and opportunities forced individuals to participate in activities located along the main transport corridors.
The association between built environment and travel behaviour has received considerable research attention in recent years. In an attempt to contribute to this growing literature, this paper investigates the connections between urban built environments and activity–travel patterns in Beijing, the capital city of China. We characterize the built environment in Beijing and establish associations between built environment and activity–travel behaviour in terms of car ownership, time spent for out-of-home activities, and daily trip frequencies and travel time. Activity diaries from 1119 respondents living in ten different neighbourhoods were collected by face-to-face interviews. A household-level structure equations model incorporating intra-household interactions is developed to analyse this data. The empirical results show that residents of different types of neighbourhoods in Beijing demonstrate significant differences in car ownership, time spent for out-of-home activities, trip rate, and travel time. Further, the characteristics of the built environment are found to have more significant impacts on the activity–travel behaviour of the male head than that of the female head.
From an exploratory factor analysis of the 2001 Hong Kong census, it was found that after the political changeover, the underlying determinants of the social dimensions were education, ethnicity, age, occupation, housing, and household size characteristics, with education being the most important, reflecting a new division of labor that has created occupational polarization and income inequality. When compared with factor analyses of the 1971 and 1981 data, a high degree of continuity is present in the social landscape. However, cluster analysis based on these determinants revealed a significant spatial mixing of population with different demographic, social, and economic characteristics, so much so that the distinction between the traditional rural areas (the New Territories) and the traditional urban areas (Hong Kong Island and Kowloon) has become blurred. Nonetheless, a strong spatial polarization has also emerged. The driving force of this spatial integration and demographic/socioeconomic change was the population decentralization policy and the new airport construction which have transformed Hong Kong into an efficient, unified, polycentric city, an international financial center that services the global economy.
This paper draws on on-going work on Hong Kong's socio-spatial structure to explore the extent to which it fits the dominant image of the global city. While there is a considerable literature on Hong Kong's changing social structure, there is relatively little on the spatial dimensions of social difference and division. The paper situates the available commentaries and analyses of Hong Kong's income, class and employment structure within the global cities debates. It then analyses census data at the tertiary planning unit level (TPU) to explore the spatial dimensions of social distance in Hong Kong. The conclusion focuses on the distinctive mediations which have shaped the socio-spatial structure of the territory. The integrative role of public housing is argued to be of particular importance in this context.
The process of developing an adequate measure of segregation occupied the literature for over a decade and culminated in the widespread use of the Index of Dissimilarity. The inadequacies of this index, although identified by the Duncans (1955), remain with us and largely have come to be ignored. This research further explores the difficulties pertaining to limitations in the use and interpretation of the Index of Dissimilarity, demonstrates some of the systematic biases resulting from these inadequacies and provides a mathematical refinement which overcomes some of the major problems inherent in the use of this index.
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Few previous studies of socio-spatial isolation have explored both its spatial and temporal dimensions. This study proposes and implemented four visual methods for analysing socio-spatial isolation using graphic representation of people's social networks and activity patterns in space and time: 3D space-time paths, time windows, 3D activity density surfaces, and ring-based visualisation of social networks. These visualisations utilise both activity-travel data and social network information. The data used were collected through a specially designed activity-travel diary survey with a sample of Koreans in the Columbus metropolitan area in Ohio (USA). The results show that these visualisations can considerably enhance our understanding of the relationships between people's activities in space-time and their social interactions. Combining social network analysis with activity pattern analysis can lead to a better understanding of socio-spatial isolation.
What could be more inherently geographical than segregation? However, the richness of the spatial variations in segregation is seldom captured by the dominant genre of empirical research. Returning the ‘geography’ to segregation research, we argue that local areas need to be given considerably more attention, using measures that explicitly reveal the spatial fabric of residential clustering along racial/ethnic lines. We first critique global measures such as the Dissimilarity Index and its spatial counterparts. Attention then turns to local measures such as the Location Quotient and Local Moran's I, applying them to Franklin County, Ohio, the core of Columbus MSA (Metropolitan Statistical Area). Our interpretation of the findings also employs local knowledge concerning neighbourhood characteristics, ongoing urban processes, historical occurrences, and the like. Thus, while local indices based on secondary data expose the terrain of clustering/segregation, follow-up fieldwork and/or secondary data analysis in a mixed-methods framework provides a better understanding of the ground-level reality of clustering/segregation. Tangible evidence of the gain from this approach is provided by our evaluation of conventional frameworks for understanding racial/ethnic aspects of residential patterning – assimilation, stratification and resurgent ethnicity – and in our proposal for a new framework, ‘market-led pluralism’, which focuses on market makers who represent the supply side of housing. Copyright © 2006 John Wiley & Sons, Ltd.
The measurement of residential segregation patterns and trends has been limited by a reliance on segregation measures that do not appropriately take into account the spatial patterning of population distributions. In this paper we define a general approach to measuring spatial segregation among multiple population groups. This general approach allows researchers to specify any theoretically based definition of spatial proximity desired in computing segregation measures. Based on this general approach, we develop a general spatial exposure/isolation index (P̃*), and a set of general multigroup spatial evenness/clustering indices: a spatial information theory index (H̃), a spatial relative diversity index (R̃), and a spatial dissimilarity index (D̃). We review these and previously proposed spatial segregation indices against a set of eight desirable properties of spatial segregation indices. We conclude that the spatial exposure/isolation index P̃*—which can be interpreted as a measure of the average composition of individuals’ local spatial environments—and the spatial information theory index H̃—which can be interpreted as a measure of the variation in the diversity of the local spatial environments of each individual—are the most conceptually and mathematically satisfactory of the proposed spatial indices.
Using an alternative conception of ethnic residential segregation, and associated statistical measures, this paper re-examines Simpson's analysis of the situation of South Asians in Bradford. It suggests that, contra Simpson, segregation of that ethnic group did increase over the period 1991–2001, with implications for public policy. In a recent paper in this journal, Ludi Simpson (2004) has challenged some of the interpre-tations of the race riots in several northern British cities in 2001, which had been linked to growing ethnic residential segregation there. That segregation, according to the com-mentators, resulted from greater self-segregation—the tendency of members of ethnic minority groups to concentrate only in certain areas of the relevant city. Against this, he claimed that "Increasing residential segregation of South Asian communities is a myth" (Simpson, 2004, p. 668), a case which he sustains by showing that net growth in the areas of South Asian concentration in Bradford was less than the sum of new arrivals and the excess of births over deaths: South Asians are dispersing through the city (a con-clusion also reached by Phillips, 1998, 2002). Simpson's argument raises a number of important questions, both semantic and tech-nical regarding the nature of segregation, which we address in this brief note.
Population segregation measurement is a topic of broad interest in the social sciences. In this paper we draw from recent advances in the spatial analysis literature to derive individualized measures of clustering and exposure. Recent research on accessibility has seen a shift from place-based measures to person-based ones. Similarly, the notion of residential clustering and exposure patterns, while typically related to the distribution of population in zonal systems, can be modified to account for heterogeneous experiences of urban space. In particular, at the individual level, the degree of clustering and exposure is related to personal mobility and the individual experience of space. In this paper we turn to the question of whether individuals belonging to different groups and living in different areas of a city observe differences in their clustering and exposure to population groups over space. The proposed procedure is applied empirically to the case of Montreal to explore how native English speakers of various levels of mobility experience exposure.
Keywords: <?tf=“t905”>activity spaces, clustering, exposure, G<sub>i</sub> * statistics, relative accessibility deprivation indicators
The paper describes the results of a pilot survey that used a one-day activity diary to collect origin-destination data, as opposed to a travel-based diary. The design of the diary is discussed, together with a comparison to a more conventional travel diary. The paper examines the extent to which the activity diary appears to have been capable of collecting good travel data that is at least comparable to travel diary efforts. In addition, a substantial portion of the paper is concerned with a comparison of the retrieval methods for the diaries. Two alternative methods were pilot-tested, one being the use of telephone retrieval and the other being mailback retrieval. Although the pilot test used small samples, the evidence appears to be strong that mailback is preferable to telephone retrieval, while telephone retrieval did not seem capable of providing some of the benefits often ascribed to it.
Travel behavior data collection methodologies have not captured the why of demand nor unsatisfied demand. This has left a gap in the development of methodology that can adequately address the role
that transport plays in fostering social exclusion. This paper presents one innovative technique that utilizes GIS to organize
and analyze data taken from focus groups and the self-mapping of individual space. Implications for transportation planning
include redefining how networks are conceived to include not just road and bus networks, but also the spatio-temporal networks
constructed by low-income people as they organize their activities.
The objective of this paper is to investigate the factors that influence distance traveled by individuals in Canadian urban areas, with a particular focus on three population segments thought to face the risk of mobility challenges: the elderly, low-income people, and members of single-parent households. Data obtained for three large urban centers – Hamilton, Toronto, and Montreal – are analyzed using spatial expansion models, a technique used to obtain spatially-varying coefficients that help to tease out contextual person-location variations in travel behavior. Detailed geographical results help to enhance our understanding of the spatiality of travel behavior of the population segments of interest. Substantively, the results provide evidence of significant interactions between location, various demographic factors, and mobility tools. More specifically, the results evince patterns of mobility that are significantly different from the mainstream population, particularly in suburban settings, in ways that are indicative of mobility challenges.
In this paper we update earlier work on racial and ethnic segregation by income to test assertions made by some observers that segregation is now largely a matter of class rather than race. Using the Summary Tape Files of the 1990 Census of Population, we measure the segregation of Blacks, Hispanics, and Asians within four categories of income: poor, lower middle class, upper middle class, and affluent. For all metropolitan areas containing at least 5000 members of the group in question, we compute indices of dissimilarity and interaction between minority members of a certain income and Whites of all income, thus measuring the extent of overall racial/ethnic segregation by social class. We find that Black residential segregation persists at high levels across all income levels, and that the gap between Blacks and other minority groups actually increases as income rises.
A major difficulty in the analysis of disaggregate activity-travel behavior in the past arises from the many interacting dimensions involved (e.g. location, timing, duration and sequencing of trips and activities). Often, the researcher is forced to decompose activity-travel patterns into their component dimensions and focus only on one or two dimensions at a time, or to treat them as a multidimensional whole using multivariate methods to derive generalized activity-travel patterns. This paper describes several GIS-based three-dimensional (3D) geovisualization methods for dealing with the spatial and temporal dimensions of human activity-travel patterns at the same time while avoiding the interpretative complexity of multivariate pattern generalization or recognition methods. These methods are operationalized using interactive 3D GIS techniques and a travel diary data set collected in the Portland (Oregon) metropolitan region. The study demonstrates several advantages in using these methods. First, significance of the temporal dimension and its interaction with the spatial dimension in structuring the daily space-time trajectories of individuals can be clearly revealed. Second, they are effective tools for the exploratory analysis of activity diary data that can lead to more focused analysis in later stages of a study. They can also help the formulation of more realistic computational or behavioral travel models.
The paper provides a first investigation of the suitability of different measures of activity space size to identify persons at risk of social exclusion. This would be a new departure for the measurement of social exclusion, which so far has relied either on aggregate measures of locations or cross-sectional data of individuals.The size of a person's activity space can only be estimated with information reflecting a longer time horizon. In this paper the six-week travel diary survey (Mobidrive) is used, which was conducted in two German cities in 1999. About 95% of all local trips were coded for 300 respondents (about 45,000 trips).The paper develops three possible measurement approaches of increasing complexity (confidence ellipse, kernel density estimates, shortest paths networks). The analysis revealed that the main driver of the size of the activity spaces is the overall number of unique locations visited by the respondents and to a lesser extent, their socio-demographic characteristics. In particular, the groups most often consider to be at risk of social exclusion (female, lower income, elderly) did not show significantly different activity spaces.
This book is about spatial problems. It provides some indication of how different people over time have solved spatial problems. It examines problems at widely different scales from individual behaviour in maginary settings or in the confines of a laboratory to the activities of corporations and governments. Topics covered include human decision making and choice behaviour at scales ranging from the individual and the private and corporate capitalist to the state. The first chapter looks at some social and behavioural constraints on human behaviour. Chapter 2 introduces a conceptual model of the individual decision-making process. It later expands this to the context of private and corporate capitalists and state decision making for planning and policy purposes. The two following chapters discuss the changing operational milieu. Initially, it examines the process and impact of economic, social; and technological change along with the practices of globalization and internationalization of economies and societies. The next chapter complements this by emphasizing the demographic and economic changes that have been instrumental in producing the decline of regions at various scales. The next three chapters discuss disaggregate behavioural processes. Chapter 10 discusses the related topics of consumer behaviour and retail center location. The role of imagery in consumer decision making is stressed as are other topics such as search and learning and feedback. Store and center images are presented as factors influencing location by retailers, and this leads to a historical overview of the development of retail centers in the US, their locational characteristics and attributes, and the physical and perceptual factors that influence consumer patronage. The affective components of place and space are discussed in Chapter 11. In Chapter 12, the more or less permanent moves across the landscape (known as migration) are discussed in both the original deterministic and alter probabilistic forms. Types of migration and the beliefs, values, aspirations, and stresses underlying the decision to move are evaluated. Chapter 13 returns to a disaggregate approach and examines residential site selection processes in the context of short-term intracity moves, a process usually referred to as mobility. The final few chapters cover relatively new topics in geography - the spatial characteristics and problems of special populations.
We develop an index of segregation based on two premises: (1) a measure of segregation should disaggregate to the level of
individuals, and (2) an individual is more segregated the more segregated are the agents with whom she interacts. We present
an index that satisfies (1) and (2) and that is based on agents' social interactions: the extent to which blacks interact
with blacks, whites with whites, etc. We use the index to measure school and residential segregation. Using detailed data
on friendship networks, we calculate levels of within-school racial segregation in a sample of U. S. schools. We also calculate
residential segregation across major U. S. cities, using block-level data from the 2000 U. S. Census.
Compared to racial segregation, economic segregation has received little attention in recent empirical literature. Yet a heated debate has arisen concerning Wilson's hypothesis (1987) that increasing economic segregation plays a role in the formation of urban ghettos. This paper presents a methodological critique of the measure of economic segregation used by Massey and Eggers (1990) and finds that it confounds changes in the income distribution with spatial changes. I develop a "pure" measure of economic segregation and present findings on all U.S. metropolitan areas from 1970 to 1990. There have been steady increases in economic segregation for whites, blacks, and Hispanics in both the 1970s and 1980s, but the increases have been particularly large and widespread for blacks and Hispanics in the 1980s. The causes of these changes are explored in a reduced form, fixed-effects model. Social distance theory and structural economic transformations do affect economic segregation, but the large increases in economic segregation among minorities in the 1980s cannot be fully explained within the model. These rapid increases in economic segregation, especially in the context of recent, albeit small, declines in racial segregation, have important implications for urban policy, poverty policy, and the stability of urban communities.
The concepts of segregation and social distance have long been used to explain the social environment of stratified residential space. However, the social significance of occupation, though acknowledged, has rarely been applied spatially. In this study, we employed these three concepts to examine the social environment of the entire metropolitan employment space as defined by job location. Smallest space analysis was used to identify and compare the sociospatial segregation produced by workers' occupational distribution in employment and residential spheres. This empirical study focused on metropolitan Tel Aviv, Israel's largest urban area, using the latest available national census. Our findings show that the social milieu of employment differed from that of residence: blue-collar workers were segregated from white-collar workers; managers, clerks, and salespersons formed the core group; and gender and ethnic divisions characterised the sociospatial realm of employment. Overall, most employees changed their social environment when they went to work. The study indicates that spatial segregation, within each sphere and between the two spheres, is intrinsic to the capitalist - patriarchal order.
Cities and metropolitan regions face several challenges including urban sprawl, auto dependence and congestion, and related environmental and human health effects. Examining the spatial characteristics of daily household activity-travel behavior holds important implications for understanding and addressing urban transportation issues. Research of this sort can inform development of urban land use policy that encourages the use of local opportunities, potentially leading to reduced motorized travel. This article examines the potential household activity-travel response to a planned metropolitan polycentric hierarchy of activity centers. Behavioral observations have been drawn from an activity-travel survey conducted in the Portland, Oregon, metropolitan area during the mid-1990s. Evidence presented from exploratory analysis indicates an urban/suburban differential, with less daily travel and smaller activity spaces for urban households. Investigation of the travel reduction potential of the proposed land-use strategy suggests that location effects could be offset by adjustments to household sociodemographic and mobility characteristics. Copyright 2006 Blackwell Publishing.
Cyberspace: Connected or segregated? From digital divide to virtual segregation
- Li F Wang
Li F and Wang D (2014) Cyberspace: Connected or segregated? From digital divide to virtual
segregation. Environment and Planning B 41: 323-340.