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Toronto’s Census Metropolitan Area (CMA) has faced on-going challenges concerning its demographic shifts in the urban and rural fringe tending to become a megacity over the coming decades, due to rapid population increase and urban amalgamation. For this research we examine past urban land use transitions in Toronto’s CMA based on collected remote...
Citations
... Toronto is located in southern Ontario and has a population estimate of 2.7 million inhabitants, being currently among the fourth largest cities in North America (Statistics Canada 2021). The population density of Toronto is 4427 inhabitants per km 2, with a prediction of continued increasing density in coming decades (Vaz and Arsanjani 2015;Statistics Canada 2021). Nevertheless, Toronto holds a large park system, including more than 1,600 parks throughout the city and other green spaces such as golf courses and sports fields, greenways, and community allotment gardens, reaching up to 8,000 hectares and representing 13.6% of the total land area of the city (City of Toronto 2017, 2019). ...
Wild bees are vital for maintaining biodiversity and food security. However, bees are currently threatened by the conversion of their natural habitat into urban areas, among many other factors. Here, we examine how five wild bee species respond to increasing urbanization according to their functional traits across the most populous city in Canada, which is also the fourth largest in North America. We investigate the effect of urbanization on bee demography and morphology as measured by abundance, sex ratio, body size, and foraging efforts. We found more bees in medium-urbanized sites and larger bees in medium and high-urbanized sites for two species (Eucera pruinosa and Ceratina calcarata). We found higher wing wear in low and medium-urbanization sites. Our data suggests that urbanization potentially affects these wild bee species’ abundance, body size, and foraging efficiency. We further discuss these findings according to the ecology of urbanization and the biology of each species.
Implications for insect conservation Human activity can significantly alter natural habitats, causing adverse effects on wild bees and ultimately affecting their survival. Considering the crucial role bees play in pollinating numerous crop and wild plant species, which, in turn, sustains biodiversity and food security, it is crucial to assess their response to the increasing levels of urbanization.
... It is the core and most complex part of the entire model construction process, which is directly related to the quality of simulation results. In this regard, scholars use various methods, including statistical analysis methods represented by the analytical hierarchy process, multi-criteria evaluation, principal component analysis, and logistic regression (Feng & Liu, 2013;Fitawok et al., 2020;Osman et al., 2016;Vaz, 2015). The statistical analysis method focuses on the numerical relationship between land use change and other factors; however, it lacks the expression of spatial state (Feng & Tong, 2017). ...
... Referring to relevant studies on the selection of driving factors (Berberoglu, Akın, & Clarke, 2016;Chakraborti, Das, Mondal, Shafizadeh-Moghadam, & Feng, 2018;Ke et al., 2016;Naghibi & Delavar, 2016;Osman et al., 2016;Qian, Xing, Guan, Yang, & Wu, 2020;Vaz, 2015;Yang et al., 2019) (Fig. 3), combined with the actual situation in the Jinpu New Area, we selected 14 layers (Table 1, Fig. 4), including the natural environment, traffic network, social infrastructure, and constraint layers. Because the research scope involves many sea areas, the global multi-resolution topography (Ryan et al., 2009), which describes the submarine topography, was selected to obtain the elevation and slope data. ...
Cellular automata (CA) is a classical method for studying land use change. However, homogeneous transformation rules have commonly been used to conduct simulations in the past, and these rules seldom consider the spatial heterogeneity of geographic elements. Therefore, in this study, we incorporated spatial-temporal het-erogeneity transformation rules into the CA framework based on spatial data mining. A model that couples self-organizing maps (SOM), hierarchical clustering (HC), and patch generation land use simulation (PLUS) was proposed; it is called the SOM-HC-PLUS model. This model considers the difference in local-area driving factors, and therefor it not only determines the optimal partition scheme automatically, but it also measures the contribution of partition driving factors. The Jinpu New Area in Dalian, China, was used to test the validity of the model by comparing the traditional PLUS model and the administrative division (AD)-PLUS model based on AD partition. The results showed that the partition scheme of the SOM-HC-PLUS model was reasonable and credible. Further, compared with other models, this model showed higher simulation accuracy and a more realistic land use distribution pattern. The driving factors showed significant differences in the overall and regional intensity. Moreover, the importance of natural environmental conditions, represented by elevation factors, in the expansion of artificial surfaces increased significantly. By 2030, artificial surfaces were projected to increase significantly through the conversion of cultivated lands. The sustainable development scenario showed a more compact patch layout and exhibited better protection of grasslands and forests than the historical development scenario. In summary, this study proposed a mixture CA model based on the idea of geographic partition, one that proved the reliability of the SOM-HC-PLUS model to conduct spatial-temporal heterogeneity studies on land use partition. It provides the possibility to explore patterns of regional land use changes over multiple periods, and can assist in urban planning and management and promoting sustainable development.
... year [4]. The metropolitan city of Toronto, located in southern Ontario, Canada, is rapidly approaching the status of megacity [5]. The levels of violent crime and homicide are both high in many of Toronto's neighbourhoods [6,7]. ...
Abstract
Objectives
Homicide rate is associated with a large variety of factors and therefore unevenly distributed over time and space. This study aims to explore homicide patterns and their spatial associations with different socioeconomic and built-environment conditions in 140 neighbourhoods of the city of Toronto, Canada.
Methods
A homicide dataset covering the years 2012 to 2021 and neighbourhood-based indicators were analysed using spatial techniques such as Kernel Density Estimation, Global/Local Moran’s I and Kulldorff’s SatScan spatio-temporal methodology. Geographically weighted regression (GWR) and multi-scale GWR (MGWR) were used to analyse the spatially varying correlations between the homicide rate and independent variables. The latter was particularly suitable for manifested spatial variations between explanatory variables and the homicide rate and it also identified spatial non-stationarities in this connection.
Results
The adjusted R2 of the MGWR was 0.53, representing a 4.35 and 3.74% increase from that in the linear regression and GWR models, respectively. Spatial and spatio-temporal high-risk areas were found to be significantly clustered in downtown and the north-western parts of the city. Some variables (e.g., the population density, material deprivation, the density of commercial establishments and the density of large buildings) were significantly associated with the homicide rate in different spatial ways.
Conclusion
The findings of this study showed that homicide rates were clustered over time and space in certain areas of the city. Socioeconomic and the built environment characteristics of some neighbourhoods were found to be associated with high homicide rates but these factors were different for each neighbourhood.
... In both Toronto and Chicago, landcover change and urbanization has impacted wetlands and facilitated wetland loss. Over the past~200 years, southern Ontario wetlands have experienced an estimated loss of more than 68%, and since the early 1980s wetland loss in Toronto and the greater Toronto area has primarily been driven by urbanization [9,10]. Additionally, over the past 150 years, wetland loss has been a major feature of landcover change in the state of Illinois, with 85% of wetlands lost in the state during this time period. ...
Wetland loss and subsequent reduction of wetland ecosystem services in the Great Lakes region has been driven, in part, by changing landcover and increasing urbanization. With landcover change data, digital elevation models (DEM), and self-organizing maps (SOM), this study explores changing landcover and the flood mitigation attributes of wetland areas over a 15-year period in Toronto and Chicago. The results of this analysis show that (1) in the city of Toronto SOM clusters, the landcover change correlations with wetland volume and wetland area range between −0.1 to −0.5, indicating that a more intense landcover change tends to be correlated with small shallow wetlands, (2) in the city of Chicago SOM clusters, the landcover change correlations with wetland area range between −0.1 to −0.7, the landcover change correlations with wetland volume per area range between −0.1 to 0.8, and the landcover change correlations with elevation range between −0.2 to −0.6, indicating that more intense landcover change tends to be correlated with spatially small wetlands that have a relatively high water-storage capacity per area and are located at lower elevations. In both cities, the smallest SOM clusters represent wetland areas where increased landcover change is correlated with wetland areas that have high flood mitigation potential. This study aims to offer a new perspective on changing urban landscapes and urban wetland ecosystem services in Toronto and Chicago.
... The CA-Markov model is a combination of a temporal prediction model (Markov chain) and a spatially dynamic model (Cellular Automata) with discrete time, space and state [29,30]. The Markov model cannot predict spatial variation, while the CA model has the advantage of simulating the spatiotemporal dynamic development of complex systems; thus, combining the two models could effectively simulate the spatiotemporal changes in land-use patterns [31,32]. ...
Assessing and predicting the evolution of habitat quality based on land use change under the process of urbanization is important for establishing a comprehensive ecological planning system and addressing the major challenges of global sustainable development. Here, two different prediction models were used to simulate the land use changes in 2025 based on the land use distribution data of Nanchang city in three periods and integrated into the habitat quality assessment model to specifically evaluate the trends and characteristics of future habitat quality changes, explore the impact of landscape pattern evolution on habitat, and analyze the differences and advantages of the two prediction models. The results show that the overall habitat quality in Nanchang declined significantly during the period 1995–2015. Habitat degradation near cities and in various watersheds is relatively significant. During the period 2015–2025, the landscape pattern and habitat quality of Nanchang will continue to maintain the trend of changes observed between 1995 and 2015, i.e., increasing construction land and decreasing habitat quality, with high pressure on ecological restoration. This study also identified that CA-Markov simulates the quantity of land use better, while FLUS simulates the spatial pattern of land use better. Overall, this study provides a reference for exploring the complex dynamic evolution mechanism of habitats.
... Hence, the evaluation of land use change is a focus It is worth mentioning that land use activity from the perspective of urban development is important, as it ensures the variety of economic growth without endangering scarce natural resources [28]; also, it equates livable and social equity within the changing aspects of current urban growth [29]. Hence, the evaluation of land use change is a focus of continuing economic theory of creating contemporary and habitable cities [30] in developing countries (e.g., Nigeria) that encounter problems in situating themselves as prospective urban centers [31]. ...
... Globally, Geographic Information Systems (GIS) integrated with Remote Sensing (RS) are potent and cost-effective tools that have been extensively used for identifying and analyzing the spatiotemporal dynamics of processes of LULC change and urban growth on general scale [32][33][34]. Some growth models include Markov chains [35], cellular automata (CA) [30,[36][37][38], spatial logistic regression [39], artificial neural networks (ANN) [40,41], and multicriteria evaluation [42]. Among the above-mentioned models, CA have been extensively utilized to assess land change and urban expansion globally [43,44]. ...
Urban growth in various cities across the world, especially in developing countries, leads to land use change. Thus, predicting future urban growth in the most rapidly growing region of Nigeria becomes a significant endeavor. This study analyzes land use and land cover (LULC) change and predicts the future urban growth of the Lagos metropolitan region, using Cellular Automata (CA) model. To achieve this, the GlobeLand30 datasets from years 2000 and 2010 were used to obtain LULC maps, which were utilized for modeling and prediction. Change analysis and prediction for LULC scenario for 2030 were performed using LCM and CA Markov chain modeling. The results show a substantial growth of artificial surfaces, which will cause further reductions in cultivated land, grassland, shrubland, wetland, and waterbodies. There was no appreciable impact of change for bare land, as its initial extent of cover later disappeared completely. Additionally, artificial surfaces/urban growth in Lagos expanded to the neighboring towns and localities in Ogun State during the study period, and it is expected that such growth will be higher in 2030. Lastly, the study findings will be beneficial to urban planners and land use managers in making key decisions regarding urban growth and improved land use management in Nigeria.
... Oztürk (2015) coupled multi-layer perceptron with CA for urban growth simulation in Samson, Turkey [24]. Vaz and Arsanjani (2015) combined MCE and CA-Markov for integrating existing and planned strategies related to urban growth of the Greater Toronto Area [25]. Wang et al. (2020) used boosted regression trees for driving methods for calibrating suitability maps for predicting land use spatial patterns by using CA [26]. ...
... Oztürk (2015) coupled multi-layer perceptron with CA for urban growth simulation in Samson, Turkey [24]. Vaz and Arsanjani (2015) combined MCE and CA-Markov for integrating existing and planned strategies related to urban growth of the Greater Toronto Area [25]. Wang et al. (2020) used boosted regression trees for driving methods for calibrating suitability maps for predicting land use spatial patterns by using CA [26]. ...
The research study utilizes Multi Criteria Evaluation (MCE) method in geographic information systems (GIS) environment and uses MCE suitability maps with Cellular Automata (CA) for predicting and simulating sustainable urban development scenarios in Famagusta City. It represents first scenario-based simulations of the future growth of Famagusta as “do-nothing” and “sustainable”. Under the do-nothing scenario, Markov Chain probability analysis with CA models is used with temporal land-use/cover datasets based on the images from 2002 and 2011. It shows that, Famagusta City is moving away from sustainable development. Future expansion of both medium-density and low-density urban zones are always located around existing built-up urban area along transportation lines. A similar model is employed by applying sustainable urban development policies by the policy driven scenario. As a main goal, sustainable urban development includes three main criteria, compactness, environmental protection, and social equity. Additionally, brownfield development, distance from center, soil characteristics, soil productivity, vegetation, environmental protection areas (EPA), distance from local services, distance from open space are used as criteria with Analytical Hierarchy Process (AHP). Having such a simulation with the combination of MCE, GIS, and CA has several advantages. Prediction of urban growth presents possible alternative development in the future; visualization of decision making easier for town planners and supports the spatial planning process; and creates more realistic results of our choices related to urban growth
... Recent advances in geocomputational methods, as well as spatial analysis, have brought new techniques that better enable the understanding of spatial characteristics of cities and regions [52]. It is of utmost importance to understand regional patterns of epidemiologic concern, to better optimize public health efficiency in rapidly changing regions [53]. In this sense, geocomputational methods, when combined with large spatially-explicit data, allow for significant contributions of regional understanding of injury dynamics. ...
Injuries have become devastating and often under-recognized public health concerns. In Canada, injuries are the leading cause of potential years of life lost before the age of 65. The geographical patterns of injury, however, are evident both over space and time, suggesting the possibility of spatial optimization of policies at the neighborhood scale to mitigate injury risk, foster prevention, and control within metropolitan regions. In this paper, Canada’s National Ambulatory Care Reporting System is used to assess unintentional and intentional injuries for Toronto between 2004 and 2010, exploring the spatial relations of injury throughout the city, together with Wellbeing Toronto data. Corroborating with these findings, spatial autocorrelations at global and local levels are performed for the reported over 1.7 million injuries. The sub-categorization for Toronto’s neighborhood further distills the most vulnerable communities throughout the city, registering a robust spatial profile throughout. Individual neighborhoods pave the need for distinct policy profiles for injury prevention. This brings one of the main novelties of this contribution. A comparison of the three regression models is carried out. The findings suggest that the performance of spatial regression models is significantly stronger, showing evidence that spatial regressions should be used for injury research. Wellbeing Toronto data performs reasonably well in assessing unintentional injuries, morbidity, and falls. Less so to understand the dynamics of intentional injuries. The results enable a framework to allow tailor-made injury prevention initiatives at the neighborhood level as a vital source for planning and participatory decision making in the medical field in developed cities such as Toronto.
... Still, there is growing concern as commercial activity has been put on hold since the end of March 2020. Furthermore, as a consequence of this growth, the region has suffered significant urban sprawl [18]. There is a growing risk of pollution as population density increases in the neighboring areas within the perimeter of the city of Toronto. ...
COVID-19 has had a significant impact on a global scale. Evident signs of spatial-explicit characteristics have been noted. Nevertheless, publicly available data are scarce, impeding a complete picture of the locational impacts of COVID-19. This paper aimed to assess, confirm, and validate several geographical attributes of the geography of the pandemic. A spatial modeling framework defined whether there was a clear spatial profile to COVID-19 and the key socio-economic characteristics of the distribution in Toronto. A stepwise backward regression model was generated within a geographical information systems framework to establish the key variables influencing the spread of COVID-19 in Toronto. Further to this analysis, spatial autocorrelation was performed at the global and local levels, followed by an error and lag spatial regression to understand which explanatory framework best explained disease spread. The findings support that COVID-19 is strongly spatially explicit and that geography matters in preventing spread. Social injustice, infrastructure, and neighborhood cohesion are evident characteristics of the increasing spread and incidence of COVID-19. Mitigation of incidents can be carried out by intertwining local policies with spatial monitoring strategies at the neighborhood level throughout large cities, ensuring open data and adequacy of information management within the knowledge chain.
... Artificial neural networks Elevation, gradient, population growth per annum, category of land parcels, propinquity to roads, service facility and built-up areas Wang and Mountrakis (2011) Cellular automata Distance to centre of the town, railways and roadways, as well as land use and terrain category Vaz and Arsanjani (2015) Decision trees Development type; elevation; gradient/slope; interior/exterior subregions; land use category; accessibility to entertainment zone; large industries; rivers/streams/canals; primary, secondary and minor roads; kernal densities of croplands; residential zones; urban expansion area; education facilities; ponds or lakes; lands for cultivation and natural green space/vegetation Jin and Mountrakis (2013) Linear/logistic regression ...
The number of city dwellers around the world is expected to increase about 2.5 billion between 2018 and 2050. This increment will lead to urban sprawl which is associated with destruction of agricultural lands, loss of fertile soils and reduction in food production. Already around 3–4% reduction of global crop production has been reported, in which Africa tops the list with 9% loss followed by Asia (5–6%). Hence, impact assessment of urban sprawl on agricultural land uses at both regional and global scale is required. The data from global satellite imageries and new geospatial technologies can play a crucial role in facilitating the impact assessments with precision and regularity. Remote Sensing (RS) and Geographic Information System (GIS) coupled with various modelling techniques have been proved to be an efficient tool for the analysis of land use/land cover (LULC). Such modelling approaches can be utilized to explore potential future impact of urban expansion on croplands and evaluate potential trade-offs between different land demands and thus are helpful for informed decision-making. This chapter emphasizes on the usage of RS and GIS to address the impact of urbanization on agricultural lands.
