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Since populations in the developing world have been rapidly increasing, accurately determining the population distribution is becoming more critical for many countries. One of the most widely used population density estimation methods is dasymetric mapping. This can be defined as a precise method for areal interpolation between different spatial un...
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This paper provides updated estimates of population size for wintering waterbirds in Great Britain using recent data and new analytical approaches for some species that use smaller inland waterbodies or the non-estuarine coast. These population estimates provide crucial baseline information that underpins the implementation of international conserv...
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... Terminologies Terminology used in this paper Region/grid set at the higher spatial level Source zones [21] Source zones Region/grid set at the lower spatial level ...
... Target zones [21] Target zones Distribution of data from a set of regions/grids at a higher spatial level to a set of regions/grids at a lower spatial level Spatial disaggregation [22], spatial downscaling [4], spatialization [23], regionalization [24] Spatial disaggregation Data to be disaggregated, for example, emissions, population, energy demand, etc. ...
... Different sampling techniques were discussed in the paper. The disaggregated data was then compared to census block-level data Karunarathne and Lee [21] disaggregated the population in a hilly area using dasymetric mapping. Here, slope, altitude, and LULC maps were employed. ...
National-level climate action plans are often formulated broadly. Spatially disaggregating these plans to individual municipalities can offer substantial benefits, such as enabling regional climate action strategies and for assessing the feasibility of national objectives. Numerous spatial disaggregation approaches can be found in the literature. This study reviews and categorizes these. The review is followed by a discussion of the relevant methods for the disaggregation of climate action plans. It is seen that methods employing proxy data, machine learning models, and geostatistical ones are the most relevant methods for the spatial disaggregation of national energy and climate plans. The analysis offers guidance for selecting appropriate methods based on factors such as data availability at the municipal level and the presence of spatial autocorrelation in the data. As the urgency of addressing climate change escalates, understanding the spatial aspects of national energy and climate strategies becomes increasingly important. This review will serve as a valuable guide for researchers and practitioners applying spatial disaggregation in this crucial field.
... Dasymetric Mapping in the context of Areal Interpolation have dominated the population downscaling related literature as they significantly improve accuracy (Langford, 2006). Ancillary information may be in any form of a categorical layer map such as Land Use Land Cover (LULC) data (Younes et al., 2023), usually acquired by remotely sensed images (Gervasoni et al., 2019;Karunarathne & Lee, 2019;Liu & Martinez, 2019;Wu et al., 2005). ...
Population data are commonly sourced from censuses, and to meet confidentiality requirements, they are spatially aggregated into standardized enumeration units. However, the need often arises to transform such datasets into user-defined spatial scales, a process known as areal interpolation. Areal interpolation is the process of data transformation across spatial zones and is particularly suitable for aggregated data such as census data. While numerous areal interpolation methods exist, a lack of implementation tools have been witnessed. In this article, we introduce PoD, a web-based solution that encompasses four downscaling schemes. To illustrate the utility of the proposed tool, we conducted a case study using actual data from the city of Mytilini, Greece. We compared the results obtained through PoD with existing R-based implementations, in addition to evaluating their performance using a reference dataset. The outcomes of this evaluation affirm the effectivenes of the proposed PoD tool over alternative implementations.
... Researchers have modeled gridded population density from small-area sampling of population counts rather than using a national census (Weber et al., 2018). To improve estimates, various dasymetric population mapping methods have used land use-land cover; climatic and topographic variables such as temperature, precipitation, elevation, and slope; and socio-economic variables such as nighttime lights, roads, and points of interest related to human activity (Karunarathne and Lee, 2019;Lloyd et al., 2019;Ye et al., 2019). Dmowska and Stepinski (2017) used dasymetric modeling with a hybrid land cover and land use map to produce a US-wide grid of population density at 30 m resolution. ...
Population change impacts almost every aspect of global change from land use, to greenhouse gas emissions, to biodiversity conservation, to the spread of disease. Data on spatial patterns of population density help us understand patterns and drivers of human settlement and can help us quantify the exposure we face to natural disasters, pollution, and infectious disease. Human populations are typically recorded by national or regional units that can vary in shape and size. Using these irregularly sized units and ancillary data related to population dynamics, we can produce high-resolution gridded estimates of population density through intelligent dasymetric mapping (IDM). The gridded population density provides a more detailed estimate of how the population is distributed within larger units. Furthermore, we can refine our estimates of population density by specifying uninhabited areas which have impacts on the analysis of population density such as our estimates of human exposure. In this study, we used various geospatial datasets to expand the existing specification of uninhabited areas within the United States (US) Environmental Protection Agency's (EPA) EnviroAtlas Dasymetric Population Map for the conterminous United States (CONUS). When compared to the existing definition of uninhabited areas for the EnviroAtlas dasymetric population map, we found that IDM's population estimates for the US Census Bureau blocks improved across all states in the CONUS. We found that IDM performed better in states with larger urban areas than in states that are sparsely populated. We also updated the existing EnviroAtlas Intelligent Dasymetric Mapping toolbox and expanded its capabilities to accept uninhabited areas. The updated 30 m population density for the CONUS is available via the EPA's Environmental Dataset Gateway (Baynes et al., 2021, https://doi.org/10.23719/1522948) and the EPA's EnviroAtlas (https://www.epa.gov/enviroatlas, last access: 15 June 2022; Pickard et al., 2015).
... Researchers have modeled gridded population density from small area sampling of population counts rather than using a national census (Weber et al., 2018). To improve estimates, various dasymetric population mapping methods have used land use/land cover, climatic and topographic variables such as temperature, precipitation, elevation, and slope, and socio-economic 70 variables such as nighttime lights, roads, and points of interest related to human activity (Karunarathne & Lee, 2019;Lloyd et al., 2019;Ye et al., 2019). Mennis and Hultgren (2006) developed an Intelligent Dasymetric Mapping (IDM) technique that estimates population density by determining class-specific representative population densities from an ancillary raster containing classes that are indicative of population dynamics. ...
Population change impacts almost every aspect of global change from land use, to greenhouse gas emissions, to biodiversity conservation, to the spread of disease. Data on spatial patterns of population density help us understand patterns and drivers of human settlement and can help us quantify the exposure we face to natural disasters, pollution, and infectious disease. Human populations are typically recorded by national or regional units that can vary in shape and size. Using these irregularly sized units and ancillary data related to population dynamics, we can produce high resolution, gridded estimates of population density through intelligent dasymetric mapping (IDM). The gridded population density provides a more detailed estimate of how the population is distributed within larger units. Furthermore, we can refine our estimates of population density by specifying uninhabited areas which have impacts on the analysis of population density such as our estimates of human exposure. In this study, we used various geospatial datasets to expand the existing specification of uninhabited areas within EPA’s EnviroAtlas Dasymetric Population Map for conterminous United States (CONUS). When compared to the existing definition of uninhabited areas for the EnviroAtlas Dasymetric Population Map, we found that IDM’s population estimates for U.S Census Bureau blocks improved across all states in CONUS. We also found that IDM performed better in states with larger urban areas than in states that are sparsely populated. Future updates of the Dasymetric Population Map might benefit from stratified (e.g., urban/exurban/rural) multi-state sampling of population density rather than state-specific sampling. We also updated the existing EnviroAtlas Intelligent Dasymetric Mapping toolbox and expanded its capabilities to accept uninhabited areas.
... Grass roots (bridging and bonding) social capital building efforts would surely help future relief efforts, and, as suggested in the analyses, may be approached in tandem with overall economic development efforts. It is also hoped that the study can help to inform policy concerning relief preparation for flooding events in the low-lying areas of the Kalu and Kelani Rivers, areas with rapidly increasing anthropogenic activities and associated soil erosion (see, [61][62][63]). Additionally, this study demonstrated the efficacy of coordinated organizational network legacies when it comes to aiding the resilience of flood victims. ...
In order to better align studies of Sri Lankan disaster management with contemporary theorizing and extant research results, this contribution provides a first-of-its-kind comparison of the structure and efficacy of social and organizational network legacies vis-à-vis flood disaster management in both rural and urban areas in Sri Lanka. A mixed methods approach is deployed. Results are based on quantitative analyses of survey data from 52 government and non-government stakeholders, in addition to qualitative input obtained during five focus group interviews and nine unstructured interviews with villagers. Social network analysis (SNA), conducted via UCINET software, examines the degree centrality, closeness centrality, and betweenness centrality of networks in three district secretariat divisions (DSDs), namely Colombo, Elapatha, and Kuruwita, areas that experienced mass flooding events in 2016 and 2017. Results suggest that the rural Kuruwita and Elapatha DSDs demonstrated denser organizational networks respectively compared to the urban Colombo DSD. This in turn led to differentiated perceptions of relief efficacy. In terms of social capital applications for disaster management studies, results suggest that bridging, bonding, and institutional considerations, along with the disaster relief expectations generated as a result, led to the creation of richer interpersonal ties and community-level organizations, particularly pronounced in rural areas, that worked in concert with broader formal networks to better address flood inundations. The study demonstrates that the development and mobilization of various actors and resources through social and organizational networks matter when ameliorating flood disaster impacts, thus imparting lessons for Sri Lanka's capital region as well as potentially areas in other countries similarly afflicted.
... The commonly used remote sensing images are as follows: high-resolution images, such as IKONOS, QuickBird, and Worldview images, and moderate-resolution images, such as the Landsat series, hyperspectral, and radar images [2,13,14]. Moreover, land-use types and building areas can be extracted from remote sensing images for population estimation [15][16][17]. Among the abovementioned remote sensing images, Landsat satellites are the most widely used because of their free access and relatively high spatial and temporal resolutions [2,16]. ...
Population data are key indicators of policymaking, public health, and land use in urban and ecological systems; however, traditional censuses are time-consuming, expensive, and laborious. This study proposes a method of modelling population density estimations based on remote sensing data in Hefei. Four models with impervious surface (IS), night light (NTL), and point of interest (POI) data as independent variables are constructed at the township scale, and the optimal model was applied to pixels to obtain a finer population density distribution. The results show that: (1) impervious surface (IS) data can be effectively extracted by the linear spectral mixture analysis (LSMA) method; (2) there is a high potential of the multi-variable model to estimate the population density, with an adjusted R2 of 0.832, and mean absolute error (MAE) of 0.420 from 10-fold cross validation recorded; (3) downscaling the predicted population density from the township scale to pixels using the multi-variable stepwise regression model achieves a more refined population density distribution. This study provides a promising method for the rapid and effective prediction of population data in interval years, and data support for urban planning and population management.
... In the above-mentioned work, the authors applied the dasymetric distribution (e.g. Eicher and Brewer, 2001;Mennis and Hultgren, 2006;Su et al., 2010;Karunarathne and Lee, 2019) in order to enhance the spatial resolution of the population potentially exposed. The dasymetric distribution can be understood, in a simple way, as a method of using ancillary information to disaggregate coarse resolution population data into a finer resolution (Mennis and Hultgren, 2006). ...
Debris flows are one of the most hazardous types of landslides in mountain regions. In the upper part of the Zêzere
valley (Serra da Estrela, Portugal) several debris flows events occurred in the last 200 years, some of them causing
loss of lives andmaterial damages. In thiswork, amethodology for pedestrian evacuationmodelling, in a debris flow
hazard scenario, was implemented. A dynamic run-outmodel, developed in previous studies, was used to evaluate
the debris flows velocities, thickness of the deposits and extent of the mobilizedmaterial. The buildings potentially
affected by the impact of debris flows were identified and the potentially exposed populationwas estimated by applying
a dasymetric distribution. The results lead to the conclusion that, in the study area, the elderly are thosewho
aremost exposed to debris flows. Furthermore, the time lapse between the debris flows initiation and the arrival at
the buildings at risk was estimated, allowing to account for the overall number of buildings where the evacuation
time takes longer than the debris flows arrival. Additionally, the safe areas within the study area were identified,
and several safe public buildings with the capacity to gather a large number of persons were selected. Considering
that the study area is located in a mountain region, characterized by steep slopes, the evacuationmodelling was performed
based on an anisotropic approach, in order to consider the influence of slope direction on travel costs. At the
end, three pedestrian evacuation travel time scenarios, based on differentwalking speeds to accommodate residents
with different ages in safer places, were compared and the results mapped. The implemented methodology is not
local dependent, which allows its reproduction elsewhere.
The aims of the research were to present the spatial structure of voting behaviour, its long-term changes in-time, and the socio-economic context of its (re)arrangement, as well as the peculiarities of post-socialist countries through the case study of the capital of Hungary – Budapest. To do this, a comprehensive and long-time database was developed as a prerequisite for the research, which included the integration of the lowest scale election results (at precinct) and census data (at census tracts) at different times in a complex metropolitan environment. This was necessary due to the nature and inner diversity of the study area, since a statistically significant result can only be obtained using an integrated database.
In order to achieve the research goal, in the first part of the dissertation I elaborated on the theoretical background of the topic, in which I described the general and then the spatial explanations and theories of voting behaviour and their embeddedness in the urban environment, all of which have been associated with socio-economic status and its diversity, the factors that most describe the urban inner spatial structure. Based on these, developed democracies were characterized by extremely stable socio-economic cleavages before the 1970s, which largely covered the voting bases of parties. Both micro- and macro-sociological and psychological-based theories agreed on the stability and further inheritance of the system through the established communication networks and relationship systems. Electoral geography joined the explanation of voting behaviour theories by embedding these explanations in the geographical space, as they were inseparable from their physical environment, even in the period dominated by the mass media at the time. In connection with this, I also developed a conceptual framework for the neighbourhood and contextual effect that has appeared less so far in the Hungarian psephology literature. Based on these, not only the different social composition of the areas influences the voting behaviour, but also the context of the given locations itself.
(...)
Overall, the dissertation sought to explore the electoral structure of the Hungarian capital, its changes, and to provide some socio-economic explanation for all of this, using a wide-ranging quantitative methodology. In addition, in many cases the election results provided additional information on the composition, political thinking and attitudes of local society. However, research also has several limitations. On the one hand, I determined the reasons behind the correlations indirectly with the help of available mathematical-statistical methods and the existing literature, but I did not use primary (even from a questionnaire survey) data to test the validation of my statements. On the other hand, the risk of ecological inference may arise in all research using a (spatially) aggregated database, which also rightly arises in the case of the present dissertation. At the same time, in my opinion, a small-scale examination of election results can provide useful – additional – information for both political science and urban geography or even urban planning in exploring conflict areas in the specific city.
Social capital is increasingly recognized as an instrumental aspect of disaster mitigation and management. The purpose of this research is to examine the empirical evidence of the evolution of social capital as well as their geographies during past flood disasters in rural and urban areas in Sri Lanka. For the case study, 405 affected households were surveyed through simple random sampling in 21 local administrative divisions in Sri Lanka. Focus group interviews and field observations were also carried out. The research is based on the mixed-research method approach and mainly relied on the qualitative data analyzing mechanism. Significant findings reveal that social capital evolved at different flood inundation phases (e.g. before, during, and after) and played a pivotal role in revivifying village livelihoods affected in past flooding events. In addition, bonding, bridging, and linking social capital strongly helped to reduce the adverse effects of past floods. Reciprocal support and resource mobilization have greatly helped secure and revive flood-affected livelihoods; examples include providing information, food, water, shelter, and other basic needs; helping with evacuation, including the recovery, transport, and return of belongings; cleaning up contaminated households and public places; and providing emotional and financial support. Moreover, geographical diversity is observed in social capital legacies and the evolution of reciprocal support.
