Spatial Regression Techniques Research Articles

An assessment of the role of safety in digital nomads' destination selections

PurposeThe safety-related features of destinations affect tourist experiences and consequently influence destination choices. This research investigates the role of spatial profile and safety in the destination choices of digital nomads.Design/methodology/approachThe study was designed using the multi-research method. To determine the spatial patterns of digital nomads' destination choices, Getis-Ord’s Gi is utilized, and spatial regression techniques are employed to ascertain the role of safety in these choices.FindingsThe main result of the research is that the most visited cities are spatially clustered in Asia, Europe and America. In this regard, digital nomads' destination choices exhibit similarities to those of traditional tourists. However, safety plays a significant role in destination preferences.Originality/valueThe research findings provide valuable insight into the relationship between digital nomads' travel preferences and safety, thereby serving as a significant source of information for destination marketing and management.

Inequality and Caste-Based Crime in India

Using data from the National Crime Records Bureau and National Sample Survey data, this paper uses a spatial regression technique to examine the effect of intercaste inequality on caste-based violence in India. Our results show that when the socioeconomic gap between the upper and lower castes decreases, upper caste violence against the lower castes increases. This result is interpreted as the upper castes reacting to changes in threat perception caused by changes in the relative positions of these two groups.

Understanding the relationship between 2D/3D variables and land surface temperature in plain and mountainous cities: Relative importance and interaction effects

The escalating prevalence of extreme heat events has intensified scholarly interest in understanding the nexus between urban built environments and extreme heat. This study focuses on the central urban areas of Chengdu and Chongqing, sizable Chinese cities that share a common building climate zone but are divergent in topography and landform (i.e., plain versus mountainous). Leveraging spatial regression, XGBoost, and Shapley additive explanation techniques, we scrutinize the spatial effects, relative importance, and interactive impacts of two-dimensional and three-dimensional (2D/3D) built environments on land surface temperature (LST). The results indicate that: (1) 2D/3D variables exert significant effects on LST, with the cooling effect of the 3D green view index outweighing that of the 2D normalized difference vegetation index (NDVI). (2) The overall relative importance of 3D variables on LST is greater than that of 2D variables in both Chengdu and Chongqing. (3) The 3D variables have particularly strong impacts on LST in mountainous cities, which are 6.21 % higher than those in plain cities. (4) 2D/3D variables interactively influence LST. For instance, LST drops in Chengdu when floor area ratio (FAR) is above 1.5 and NDVI is below 0.53, as well as when FAR is less than 1 and NDVI is greater than 0.53. Our findings provide support and recommendations for location-specific sustainable urban development and effective heat adaptation strategies.

The geography of (un)reasonable suspicion: Rethinking causes of racial disparities in police stops

AbstractIn Illinois vs. Wardlow (2000), the U.S. Supreme Court ruled that presence in a “high‐crime” area is one factor that police can consider when establishing reasonable suspicion to justify a Terry stop. Some legal scholars argue that through this decision the Court propagated inequity in police stops by setting a lower evidentiary standard for establishing reasonable suspicion in neighborhoods with greater numbers Black residents, which are more likely than White neighborhoods to be considered crime hot spots. To assess these claims, I analyze pedestrian stop data from the Chicago Police Department for the years 2016 and 2017. Using spatial regression techniques, I evaluate relationships between neighborhood measures of Black disadvantage, police stop justifications, and “hit rates” of stops. The results suggest that reasonable suspicion is uniquely constructed in disadvantaged Black neighborhoods but that this does not result in significantly different enforcement rates. Based on these results, I argue that policing scholars must reconsider sources of inequity in policing and, in particular, consider the role of the law in shaping these outcomes.

Conceptual Framework for Maximizing Service Catchment Area Between Rail Transit and Feeder Service Using Spatial Temporal Regression Technique

The move towards a more sustainable transport mode is crucial in the Klang Valley by incorporating feeder services to rail network stations. Feeder services will complement the expanding rail transits network and deliver connectivity for areas not covered within rail stations. Past literatures found that feeder services were ineffective during rail breakdown as feeder service was not going or stopping to where it was intended to. Feeder service can be allocated more effectively if we have better idea of targeted service area. Urban planners now have access to a variety of urban analytics because of technological advancements and the rise of smart cities. Data generated are produced across numerous locations and at diverse time scales, providing a significant possibility for data mining to uncover insightful information when both space and time are considered. This study will attempt to assess the community’s accessibility to rail transit through feeder service in Klang Valley. In achieving this a conceptual framework is developed within the objectives of this study which are firstly, to identify built environment factors that influence ridership around transit stations. Secondly, to determine the spatial -temporal relationship between built environment and transit ridership of feeder service and rail transits. Next, the station-level spatial and temporal variability of ridership data between feeder service to rail network is examined. Finally, as the main finding of this study, a framework in maximizing a service catchment transportation service for feeder service and rail transit will be developed. In doing that, the conceptual framework approach is by addressing numerical analytical technique in geographic data, by using spatial temporal regression. This study aspires to provide planning departments and transit agencies with useful guidance to execute targeted policies and develop a sustainable and efficient feeder service coverage area to support rail transits by looking at both spatial and temporal scales.

Large-scale assessment of PFAS compounds in drinking water sources using machine learning.

The monitoring of Per and Polyfluoroalkyl substances (PFAS) in drinking water sources has significantly increased due to their recognition as a major public health concern. This information has been utilized to assess the importance of potential explanatory variables in determining the presence and concentration of PFAS in different regions. Nevertheless, the significance of these variables and the reliability of the methods in regions beyond where they were initially tested is still uncertain. Hence, our research pursues two main objectives: 1) to evaluate the validity of the aforementioned variables and methods for several PFAS species in a different area and 2) to build on existing modeling work; a new PFAS predictive model is introduced which is more reliable in determining the presence and concentration of PFAS at a regional level. To achieve these goals, we reconstructed four state-of-the-art models using a statewide dataset available for Michigan. These models involve spatial regression techniques, classification and regression random forest algorithms, and boosted regression trees. They also include numerous explanatory variables, such as features of local soil and hydrology and the number of nearby contamination sources. Then, we use a Bayesian selection approach to find the most relevant among these variables. Finally, we employ the most relevant covariates to assess PFAS occurrence and estimate their concentration using a novel combination of machine learning algorithms and conditional autoregressive (CAR) modeling. In the first case, PFAS occurrence was assessed with an accuracy comparable to the reconstructed models (>90%) while using significantly fewer variables. In the second case, by maintaining low data requirements, the estimated concentrations of most PFAS compounds were more closely aligned with available observations compared to previous methods, with correlation coefficients ρ > 0.90 and R2 > 0.77.

Modelling the effect of spatial determinants on freight (trip) attraction: A spatially autoregressive geographically weighted regression approach

This paper investigates the effect of spatial and locational characteristics of establishments on freight (trip) attraction (FA/FTA) models. The authors estimated econometric models of FA and FTA as a function of the establishment attributes as well as the spatial and locational determinant variables, using establishment-level data collected from Addis Ababa City, Ethiopia. The interconnected issues of spatial dependency and spatial heterogeneity, together with nonlinear specifications, were incorporated with the application of spatial techniques, including spatial error models (SEM), spatial autoregressive model (SAR), geographically weighted regression (GWR), multiscale-GWR (MGWR), and the combination GWR-SAR/MGWR-SAR. Regarding the explanatory variables, the empirical results revealed that firms in the manufacturing, wholesale and retail sectors located on the wider streets tend to receive more FA and FTA. The closeness to the primary road network and the city entry gate influences the FTA of manufacturing and construction firms. Moreover, retail establishments near the major market tend to receive more tonnage. The models also confirm that FA and FTA are the results of two different processes. Overall, the use of spatial regression techniques improves the accuracy of both FA and FTA models. MGWR-SAR exhibits superior performance by jointly addressing spatial dependency and heterogeneity. The MGWR-SAR model also uncovers the local variability of the variables representing the spatial and locational effects on freight attraction. The methodological analysis and empirical findings of the study could provide useful insights to support urban freight modelling, planning, and decision-making.

Crime against Women in India: A Spatio-Temporal Analysis

Using the National Crime Record Bureau and National Sample Survey data, this article explores the spatial patterns of different types of crime against women (CAW) in India. To explain these spatial patterns, we use spatial panel data regression techniques that account for spatial dependencies between different CAW and their socio-economic predictors at the NSS-region level. The results show that different types of CAW have different time-consistent spatial clustering of high and low crime zone. Evidence has been found that the prevalence of CAW in a region is influenced by the CAW rate of its neighboring regions. This influence is linked to the spatial diffusion of harmful gender norms that spread across contiguous regions over time. Our results also confirm the importance of various women empowerment-related variables like female literacy rate, sex ratio, and female labor force participation rate in reducing the violence against women. These findings could have significant implications for the policies aiming to reduce CAW.

Neighborhood characteristics encouraging micromobility: An observational study for tourists and local users

This paper provides insight on the factors associated with shared micromobility usage in urban areas exploring the influence of socio-demographic factors and elements of the built environment at a fine level of analysis using spatial regression techniques. In this context, this study aims to extend existing knowledge on the subject differentiating from existing literature in three aspects: 1) conducting an analysis in a European city, and particularly a city in France, which has been a pioneer in the use of micromobility services; 2) Exploring differences between trips made by locals and trips made by foreign tourists revealing variations in usage patterns; 3) Introducing new variables associated with urban density and parking availability, as well as aesthetic attractiveness and touristic attractions. Results show that areas with increased percentage of young population present higher e-scooter trip density. Average income also has a positive effect, and this effect is higher in parts of the city where low incomes are observed. The presence of public transport stations, as well as shops and restaurants, increase e-scooter rides in an area. Local roads and cycling infrastructure encourage e-scooter usage, with this effect being stronger for areas in proximity to some major road segment. Finally, high density areas and areas where finding parking is difficult and costly, are also associated with higher e-scooter use. Results can help urban planners in local policy implementation, and shared mobility operators in vehicle redistribution strategy and fleet sizing.

Poultry manure gleaned antibiotic residues in soil environment: A perspective of spatial variability and influencing factors

The antibiotics released by human and animals end up in the environmental sinks like soil and water to cause contamination and induce resistance in the microflora. The knowledge of fate and behavior of antibiotics in diverse geographical, climatological, and physicochemical is limited. Therefore, present study investigated the spatial distribution of antibiotics and their relationship with various factors and the source-sink relationship between soil and poultry manure. This was achieved by employing spatially constrained hierarchical clustering, global and local spatial autocorrelation, and spatial regression techniques. Most of the antibiotics co-occurred in both soil and poultry manure matrices, however antibiotic concentration in soil (1.20 μg kg−1 < antibiotics ≤21.38 μg kg−1) was lower than that in the poultry manure (7.05 μg kg−1< antibiotics ≤60.2 μg kg−1). Majority of the antibiotics showed spatial independence in both poultry manure and soil, except for sulfadiazine, sulfanilamide and sulfapyridine with Moran's I > − 0.111. Local indicator of spatial association indicated localized spatial clustering and outlier behavior of antibiotics. The underlying reasons for spatial heterogeneity of antibiotics resolved by spatial regression models indicated elevation, S%, C%, pH and mean annual temperature as the major factors. The influence of antibiotic concentration in poultry manure as a source was significant but marginal compared to the other predictors of spatial heterogeneity.

Exploring Local Labor Markets and Knowledge Spillover from a Spatial Perspective

Relevance. Cities serve as vital hubs for socioeconomic activities, fostering convergence in local labor markets characterized by high productivity, premium wages, and knowledge spillover. Jakarta Metropolitan, as Indonesia's economic center, embodies these advantages, prompting the need for a spatial investigation of their regional association. Research Objective. This study examines the unique attributes of the labor market in the Jakarta Metropolitan Area. The analysis encompasses anomalies such as the impact of premium wages on local labor productivity and the influence of education levels, as a proxy for knowledge spillover, on urban labor productivity. Data and Methods. Utilizing microdata from Sakernas and macrodata from BPS for 2017-2019, this study offers a comprehensive analysis of the Jakarta Metropolitan Area. By synthesizing cross-level data, the intricate interplay between productivity, wages, and area size becomes evident, particularly the tendency for skilled individuals to gravitate toward larger urban centers. Employing spatial regression, the analysis takes into account local characteristics in regions. Results. Density and education have a positive correlation, while the number of holders of bachelor's degrees has a surprisingly negative impact on productivity. Notably, higher aggregate education levels enhance regional productivity throughout the Jakarta Metropolitan Area, except for highly educated individuals. The level of education influences the minimum wage in a region, driven by spatial disparities in the educational infrastructure and quality. The number of educated people also influences wages in and across regions, prompting migration to regions with higher salaries. Conclusions. By integrating microdata and macrodata and employing spatial regression techniques, this study shows the connection between education, productivity, and regional dynamics, particularly in the Jakarta Metropolitan Area. These findings challenge the assumption that possessing a high level of education guarantees higher productivity and remuneration, demonstrating the need for education reforms that align with labor market demands and bolster the economy.

Exploration of spatial clustering in maternal health continuum of care across districts of India: A geospatial analysis of demographic and health survey data.

The continuum of care (CoC) throughout pregnancy, delivery and post-delivery has recently been highlighted as an integrated intervention programme for maternal, new-born, and child health. Existing literature suggests the importance of continuum of care (CoC) for improved maternal and child health outcomes. However due to unavailability of data at the lowest administrative levels, literature on spatial pattern of uptake of full CoC is lacking. The present study attempts to focus on the spatial analysis of CoC in maternal health care in India. The study is based on the fourth round of National Family Health Survey data conducted in 2015-16 in India. The outcome variable used is maternal health continuum of care which includes- at least 4 ANC visits, delivery through skilled birth attendant and postnatal check-up within 48 hours of delivery. Univariate and bivariate Local Indicator of Spatial Association (LISA) maps have been generated to show the spatial pattern of CoC across 640 districts in India. We also employed spatial regression techniques to explore the determinants of CoC. Percentage of women who followed full CoC was observed to be least for East Kameng (0.0%) district of Arunachal Pradesh and highest in North Goa district (90.4%). Majority of districts where uptake of full CoC was more than 80 percent were found concentrated in southern region on India. Equivalently, findings indicated a strong spatial clustering of full CoC with high-high clusters mostly concentrated in southern districts. Low-low district clusters are concentrated in the states of Uttar Pradesh, Bihar and Madhya Pradesh. For complete CoC the global Moran's I is 0.73 indicating the spatial dependence. The spatial regression analysis suggested that modern contraceptive use, meeting with health worker, urbanization and secondary or above education for women have positive impact on the utilisation of CoC. The spatial pattern indicates district level clustering in uptake of CoC among women. The study suggests policymakers and stakeholders to implement comprehensive interventions at sub-regional levels for ensuring the completion of CoC for women which acts as a preventive measure for adverse outcomes such as-maternal and child mortality.

Spatial variability of mother-to-child human immunodeficiency virus transmission in a province in the Brazilian Rainforest: An ecological study.

The mother-to-child transmission (MTCT) of the human immunodeficiency virus (HIV) remains a serious public health problem in the Brazilian Rainforest. This study aimed to spatially analyze this type of infection between 2007 and 2018 in Pará, which is the second-largest Brazilian state in the Brazilian Rainforest and also has the highest MTCT of HIV in Brazil. We analyzed the incidence rates of HIV (including the acquired immunodeficiency syndrome (AIDS) by MTCT as the main route of infection in children younger than 13 years old and whose mothers live in Pará. We employed spatial autocorrelation, spatial scanning, and geographic-weighted spatial regression techniques. In the period of this study, 389 new HIV/AIDS were noted, with territorial expansion of the incidence rates in the municipalities in northern and southern Pará having the highest rates. São Francisco do Pará had high spatial risk and high-spatiotemporal risk clusters comprising municipalities in western and south-western Pará between 2013 and 2016. The spatial variability of HIV/AIDS incidence rates was found to be common in the number of men and women with formal jobs; unemployed ≥18 years old people; elementary school pupils; and families enrolled in the "Single Registry for Social Programs". The social equity approach in Pará guarantee pregnant women access to preventive, diagnostic and treatment health services and their children should be supported to eliminate the MTCT of HIV in Pará.

Urban–Rural Disparities in Deaths of Despair: A County-Level Analysis 2004–2016 in the U.S.

The purpose of this study is to examine nationwide disparities in drug, alcohol, and suicide mortality; evaluate the association between county-level characteristics and these mortality rates; and illustrate spatial patterns of mortality risk to identify areas with elevated risk. The authors applied a Bayesian spatial regression technique to investigate the association between U.S. county-level characteristics and drug, alcohol, and suicide mortality rates for 2004-2016, accounting for spatial correlation that occurs among counties. Mortality risks from drug, alcohol, and suicide were positively associated with the degree of rurality, the proportion of vacant housing units, the population with a disability, the unemployed population, the population with low access to grocery stores, and the population with no health insurance. Conversely, risks were negatively associated with Hispanic population, non-Hispanic Black population, and population with a bachelor's degree or higher. Spatial disparities in drug, alcohol, and suicide mortality exist at the county level across the U.S. social determinants of health; educational attainment, degree of rurality, ethnicity, disability, unemployment, and health insurance status are important factors associated with these mortality rates. A comprehensive strategy that includes downstream interventions providing equitable access to healthcare services and upstream efforts in addressing socioeconomic conditions is warranted to effectively reduce these mortality burdens.

The Effect of Proximity to Urban Rail on Housing Prices in Ottawa

Increasingly, urban rail transit (URT) is seen as a desirable solution for transportation challenges faced by both urban planners and residents of suburban areas alike. The availability and ease of access to URT, in turn, may result in distortions in local real estate markets. The conventional wisdom, in fact, suggests that construction of urban rail lines serves as a magnet for new housing development and, in turn, can lead to increases in property values in proximity to URT stations. Existing studies have, in good measure, confirmed this belief, but largely on the basis of global area studies that can often mask locally differentiating factors affecting housing prices. Using data from the City of Ottawa, this study seeks to move beyond such analyses by using spatial regression and mapping techniques that reveal that the relationship between URT stations and housing prices is far more complex than is commonly believed. The study demonstrates that while at the macro-level housing prices do vary positively with proximity to URT stations, the relationship is spatially dependent and may be affected by factors unique to specific locales.

The Temporal and Spatial Epidemiology Employed in the Elimination of the HIV Epidemic in the Largest Capital of the Brazilian Rainforest.

Background: The main goal of this study was to analyze the human immunodeficiency virus (HIV) epidemic temporally and spatially in Belém from 2007 to 2018. Methods: The incidence rates were analyzed according to time using autoregressive integrated moving-average models, as well as spatially using spatial autocorrelation, Kernel density, scan statistics, and regression techniques. Results: During the study period, 6007 notifications of new cases of HIV/AIDS were reported. The time series analysis revealed a stabilized trend of incidence from 2007 to October 2016, followed by irregular fluctuations until the end of December 2018. Seasonal behavior was observed from 2019 to 2022. The high–high incidence clusters were found in the central and transition areas. An expansion of the number of new reported cases was observed in the central area. Three spatial risk zones were observed. The higher relative risk zone was concentrated in the transition area. The spatial regression showed that the incidence rates were positively correlated with the Family Health Strategy (FHS) coverage. Conclusions: To eliminate HIV in Belém, it will be necessary to decentralize testing and ART and expand the coverage of FHS to ensure universal access to healthcare for citizens.

Community solutions to food apartheid: A spatial analysis of community food-growing spaces and neighborhood demographics in Philadelphia

Black and low-income neighborhoods tend to have higher concentrations of fast-food restaurants and low produce supply stores. Limited access to and consumption of nutrient-rich foods is associated with poor health outcomes. Given the realities of food access, many members within the Black communities grow food as a strategy of resistance to food apartheid, and for the healing and self-determination that agriculture offers.In this paper, we unpack the history of Black people, agriculture, and land in the United States. In addition to our brief historical review, we conduct a descriptive epidemiologic study of community food-growing spaces, food access, and neighborhood racial composition in present day Philadelphia. We leverage one of the few existing datasets that systematically documents community food-growing locations throughout a major US city. By applying spatial regression techniques, we use conditional autoregressive models to determine if there are spatial associations between Black neighborhoods, poverty, food access, and urban agriculture in Philadelphia.Fully adjusted spatial models showed significant associations between Black neighborhoods and urban agriculture (RR: 1.28, 95% CI = 1.03, 1.59) and poverty and urban agriculture (RR: 1.27, 95% CI = 1.1, 1.46). The association between low food access and the presence of urban agriculture was generally increased across neighborhoods with a higher proportion of Black residents.These results show that Philadelphia neighborhoods with higher populations of Black people and neighborhoods with lower incomes, on average, tend to have more community gardens and urban farms. While the garden data is non-temporal and non-causal, one possible explanation for these findings, in alignment with what Philadelphia growers have claimed, is that urban agriculture may be a manifestation of collective agency and community resistance in Black and low-income communities, particularly in neighborhoods with low food access.

Bandwidth Selection in Geographically Weighted Regression Models via Information Complexity Criteria

The geographically weighted regression (GWR) model is a local spatial regression technique used to determine and map spatial variations in the relationships between variables. In the GWR model, the bandwidth is very important as it can change the parameter estimates and affect the model performance. In this study, we applied the information complexity (ICOMP) type criteria in the selection of fixed bandwidth for the first time in the literature. The ICOMP-type criteria use a complexity measure that measures how parameters in the model relate to each other. A real dataset example and a simulation study have been conducted. Results of the simulation demonstrate that GWR models created with the bandwidth selection by ICOMP-type criteria show superior performance. In addition, when the bandwidth is selected according to the ICOMP-type criteria and the GWR model is created for the actual total fertility rate data, it is seen that the spatial distribution of the total fertility rate estimates is quite compatible with the distribution of the actual total fertility rate. According to the results, ICOMP-type criteria can be used effectively instead of the classical criteria in the literature in the selection of bandwidth in the GWR model.

Temporal trend and spatial analysis of the HIV epidemic in young men who have sex with men in the second largest Brazilian Amazonian province

BackgroundAfter 40 years of its starting, the HIV epidemic in Brazilian Amazon region remains on an increasing trend. The young men who have sex with men (MSM) have been the most impacted by the HIV in the last decade. However, much more than attributing the risk behavior to HIV uniquely to the individual, behaviors are shaped by social determinants of health (SDH). Despite the problem, there is a scarcity of studies evaluating the impact of SDH on HIV among young MSM and none of them were done in the Northern of Brazil. Therefore, the main goal of this study was to analyse the HIV epidemic among Brazilian Amazonian young MSM using temporal trends and spatial analysis.MethodsWe conducted an ecological study using reported cases of HIV/AIDS in young MSM living in Pará, the second larger Brazilian Amazonian province, between 2007 and 2018. Data were obtained from the Information System for Notifiable Diseases. For the temporal analysis, we employed a Seasonal and Trend decomposition using Loess Forecasting model (STLF), which is a hybrid time-series forecast model, that combines the Autoregressive-Integrated Moving Average (ARIMA) forecasting model with the Seasonal-Trend by Loess (STL) decomposition method. For the spatial analysis, Moran’s spatial autocorrelation, spatial scan, and spatial regression techniques were used.ResultsA total of 2192 notifications were included in the study. Greater variabilities in HIV/AIDS population-level diagnosis rates were found in the festive months. The HIV/AIDS population-level diagnosis rates exhibited an upward trend from 2013 and this trend is forecasted to continue until 2022. Belém, the capital of Pará, presented the highest spatial risk for HIV/AIDS and was the only city to present spatiotemporal risk from 2014 to 2018. The geographic variation of the HIV epidemic was associated with the number of men with formal jobs, the average salary of men, and the percentage of people over 18 years old with elementary education.ConclusionThe upward trend of HIV/AIDS population-level diagnosis rate forecasted until 2022 and the variability of the epidemic promoted by the SDH brings an alert and subsidies to health authorities to implement more efficient and focalized public policies against HIV among young MSM in Pará.

Exploring spatial heterogeneity and environmental injustices in exposure to flood hazards using geographically weighted regression

This study explores flood-related environmental injustices by deconstructing racial, ethnic, and socio-demographic disparities and spatial heterogeneity in the areal extent of fluvial, pluvial, and coastal flooding across Canada. The study integrates JBA Risk Management's 100-year Canada Flood Map with the 2016 national census-based socioeconomic data to investigate whether traditionally recognized vulnerable groups and communities are exposed inequitably to inland (e.g., fluvial and pluvial) and coastal flood hazards. Social vulnerability was represented by neighbourhood-level socioeconomic deprivation, including economic insecurity and instability indices. Statistical analyses include bivariate correlation and a series of non-spatial and spatial regression techniques, including ordinary least squares, binary logistic regression, and simultaneous autoregressive models. The study emphasizes the quest for the most appropriate methodological framework to analyze flood-related socioeconomic inequities in Canada. Strong evidence of spatial effects has motivated the study to test for the spatial heterogeneity of covariates by employing geographically weighted regression (GWR) on continuous outcome variables (e.g., percent of residential properties in a census tract exposed to flood hazards) and geographically weighted logistic regression on dichotomous outcome variables (e.g., a census tract in or out of flood hazard zone). GWR results show that the direction and statistical significance of relationships between inland flood exposure and all explanatory variables under consideration are spatially non-stationary. We find certain vulnerable groups, such as females, lone-parent households, Indigenous peoples, South Asians, the elderly, other visible minorities, and economically insecure residents, are at a higher risk of flooding in Canadian neighbourhoods. Spatial and social disparities in flood exposure have critical policy implications for effective emergency management and disaster risk reduction. The study findings are a foundation for a more detailed investigation of the disproportionate impacts of flood risk in Canada.