Uncertain Geographic Context Problem Research Articles

Historical Bias in Mortgage Lending, Redlining, and Implications for the Uncertain Geographic Context Problem: A Study of Structural Housing Discrimination in Dallas and Boston.

According to the uncertain geographic context problem, a lack of temporal information can hinder measures of bias in mortgage lending. This study extends previous methods to: (1) measure the persistence of racial bias in mortgage lending for Black Americans by adding temporal trends and credit scores, and (2) evaluate the continuity of bias in discriminatory areas from 1990 to 2020. These additions create an indicator of persistent structural housing discrimination. We studied the Boston-Cambridge-Newton and Dallas-Fort Worth metropolitan statistical areas to examine distinct historical trajectories and urban development. We estimated the odds of mortgage denial for census tracts. Overall, all tracts in Boston-Cambridge-Newton (N = 1003) and Dallas-Fort Worth (N = 1312) displayed significant change, with greater odds of bias over time in Dallas-Fort Worth and lower odds in Boston-Cambridge-Newton. Historically redlined areas displayed the strongest persistence of bias. Results suggest that temporal data can identify persistence and improve sensitivity in measuring neighborhood bias. Understanding the temporality of residential exposure can increase research rigor and inform policy to reduce the health effects of racial bias.

Next track point prediction using a flexible strategy of subgraph learning on road networks

Accurately predicting the next track point of vehicle travel is crucial for various Intelligent Transportation System (ITS) applications, such as travel behavior studies, traffic control, and traffic congestion monitoring. Recent works on trajectory prediction follow a paradigm that first represents the raw trajectory and subsequently makes predictions based on that representation. Currently, trajectory representation methods tend to project trajectory points to road networks by map matching and represent trajectories based on the representation of matched roads. However, precisely matching trajectories to roads is a challenge in ITS, as the matching precision is greatly affected by the quality of the trajectory. Meanwhile, since it is difficult to discern whether trajectory matching results are accurate or confounded, how to effectively utilize this type of uncertain geographic context information is also a challenge, which is defined as the Uncertain Geographic Context Problem (UGCoP) in geographic information science. Therefore, we propose a flexible strategy of subgraph learning, referred to as SLM, for predicting the next track point of vehicles. Specifically, a subgraph generation module is first proposed to extract topology contextual information of the roads around historical trajectory points. Secondly, a subgraph learning module is designed to learn rich spatial and temporal features from generated subgraphs. Finally, the extracted spatiotemporal features will be fed into a prediction module to predict the next track points of vehicles on road networks. Our model enables the effective utilization of uncertain geographic context information of trajectories on road networks while avoiding the error brought by map matching. Extensive experiments based on trajectory datasets in two different cities confirm the effectiveness of our approach.

Mobility-oriented measurements of people's exposure to outdoor artificial light at night (ALAN) and the uncertain geographic context problem (UGCoP).

Advanced nighttime light (NTL) remote sensing techniques enable the large-scope epidemiological investigations of people's exposure to outdoor artificial light at night (ALAN) and its health effects. However, multiple uncertainties remain in the measurements of people's exposure to outdoor ALAN, including the representations of outdoor ALAN, the contextual settings of exposure measurements, and measurement approaches. Non-exposed but included outdoor ALAN and causally irrelevant outdoor ALAN may manifest as contextual errors, and these uncertain contextual errors may lead to biased measurements and erroneous interpretations when modeling people's health outcomes. In this study, we systematically investigated outdoor ALAN exposure measurements in different geographic contexts using either residence-based or mobility-oriented measurements, different spatial scales, and multiple NTL data sources. Based on the GPS data collected from 208 participants in Hong Kong, outdoor ALAN exposures were measured from NTL imagery at 10 m, 130 m, and 500 m spatial resolutions using in-situ methods or 100 m, 300 m, and 500 m buffer zone averaging. Descriptive analysis, multiple t-tests, and logistic regression were employed to examine the differences between outdoor ALAN exposure measurements using various contextual settings and their effects on modeling people's overall health. Our results confirmed that different contextual settings may lead to significantly different outdoor ALAN exposure measurements. Our results also confirmed that contextual errors may lead to erroneous conclusions when using improper contextual settings to model people's overall health. Consequentially, we suggest measuring people's exposure to outdoor ALAN using the mobility-oriented approach, NTL representation with the high spatial resolution, and a very small buffer zone as a contextual unit to derive outdoor ALAN exposure. This study articulates essential methodological issues induced by uncertainties in outdoor ALAN exposure measurements and can provide essential implications and suggestions for a broad scope of studies that need accurate outdoor ALAN exposure measurements.

A Spatial Network-Based Assessment of Individual Exposure to COVID-19

This study seeks to examine the possible impacts of sociodemographic factors, individual mobility patterns, and daily activities on individual exposure to COVID-19 risk when assessed by different risk measures. Taking Hong Kong as the study area, we first model the risk of COVID-19 using a density-based approach and a network-based approach and reveal the differences in the spatial distributions of COVID-19 transmission risk they obtained. Then, using two-day individual Global Positioning System trajectory data and travel diaries collected from individuals in two communities, we measure individual exposure to COVID-19 transmission risk based on their mobility patterns and reveal the disparities in COVID-19 risk exposure among residents of different demographic groups and neighborhoods when conducting different activities. This study reveals the differences in the spatial patterns of COVID-19 transmission risk when the risk is conceptualized by density and network. It demonstrates the power of a spatial network approach in enhancing our understanding of individual exposure to infectious diseases. This study also advances the existing literature by exploring COVID-19 risk exposure, considering individual daily mobility and addressing the uncertain geographic context problem in analyzing individual COVID-19 risk exposure.

Varying Built Environment Contexts and Trip Chain Decisions: A Multinomial-Choice Gradient Boosting Decision Trees Analysis

Understanding daily trip chain decisions is important for facilitating sustainable travel behavior and reducing traffic congestion. However, the relationships between varying built environment contexts across activity locations and trip chain choices remain unclear. This study adopts a multinomial-choice gradient boosting decision trees (MC-GBDT) model to compare the relative importance of socioeconomic variables with built environment variables. Additionally, it investigates the nonlinear and threshold effects between residential areas and primary activity areas. In the 2017 Beijing household activity survey, socioeconomic variables account for only about 21% of the relative importance in predicting the trip chain choices, whereas built environment variables constitute 78%, including 46% attribute to the primary activity area and 32% to the residential area. Moreover, several built environment variables have distinctive nonlinear and threshold effects, including local access to daily facilities, road density, and transit accessibility measures. Street design features and access to public transit have greater impacts on trip chain decisions in both residential and primary activity areas. These findings echo the uncertain geographic context problem (UGCoP) and imply that traditional built environment-trip chain research might underestimate the impact of built environment contexts at primary activity locations.

The uncertain geographic context problem (UGCoP) in measuring people’s exposure to green space using the integrated 3S approach

The “mobility turn” in environmental health studies promoted the integrated 3S (GIS, GPS, and remote sensing) approach in the study of urban residents’ exposure to green space and corresponding health outcomes. However, few studies have examined the uncertainty induced by contextual settings when measuring people’s exposure to green space using the conventional and integrated 3S approaches. In this paper, we compared the differences in green space exposure obtained from different geographic contexts using residence-based and mobility-based methods, multiple spatial resolutions, and buffer zones. We collected 7-day GPS trajectories from 208 participants at the 1-minute temporal resolution in Hong Kong. Entire Hong Kong’s green space was delineated using multiple remote sensing data sources at 3 m, 10 m, and 30 m spatial resolutions. Lastly, the residence-based and mobility-based measurements of exposure to green space were calculated for each participant using 100 m, 300 m, and 500 m buffer zones at three spatial resolutions. Descriptive analyses, t-tests, and logistic regression were employed to examine the influence of different contextual settings on different measurements of green space exposure and their health effects. The results indicate multiple significant disparities. The most striking difference is that mobility-based measurements of exposure to green space are significantly higher than those of residence-based measurements, manifesting the uncertain geographic context problem (UGCoP). For future studies, we suggest using mobility-based measurements of exposure to green space, smaller buffer zones, and finer spatial resolutions, which enable more accurate measurements of green space exposure for the study of green space’s health effects.

Comparing effects of Euclidean buffers and network buffers on associations between built environment and transport walking: the Multi-Ethnic Study of Atherosclerosis

BackgroundTransport walking has drawn growing interest due to its potential to increase levels of physical activities and reduce reliance on vehicles. While existing studies have compared built environment-health associations between Euclidean buffers and network buffers, no studies have systematically quantified the extent of bias in health effect estimates when exposures are measured in different buffers. Further, prior studies have done the comparisons focusing on only one or two geographic regions, limiting generalizability and restricting ability to test whether direction or magnitude of bias are different by context. This study aimed to quantify the degree of bias in associations between built environment exposures and transport walking when exposures were operationalized using Euclidean buffers rather than network buffers in diverse contexts.MethodsWe performed a simulations study to systematically evaluate the degree of bias in associations between built environment exposures in Euclidean buffers and network buffers and transport walking, assuming network buffers more accurately captured true exposures. Additionally, we used empirical data from a multi-ethnic, multi-site cohort to compare associations between built environment amenities and walking for transport where built environment exposures were derived using Euclidean buffers versus network buffers.ResultsSimulation results found that the bias induced by using Euclidean buffer models was consistently negative across the six study sites (ranging from -80% to -20%), suggesting built environment exposures measured using Euclidean buffers underestimate health effects on transport walking. Percent bias was uniformly smaller for the larger 5 km scale than the 1 km and 0.25 km spatial scales, independent of site or built environment categories. Empirical findings aligned with the simulation results: built environment-health associations were stronger for built environment exposures operationalized using network buffers than using Euclidean buffers.ConclusionThis study is the first to quantify the extent of bias in the magnitude of the associations between built environment exposures and transport walking when the former are measured in Euclidean buffers vs. network buffers, informing future research to carefully conceptualize appropriate distance-based buffer metrics in order to better approximate real geographic contexts. It also helps contextualize existing research in the field that used Euclidean buffers when that were the only option. Further, this study provides an example of the uncertain geographic context problem.

Urban livability and contextual uncertainties: An assessment of livability through the lens of urban dwellers in Guwahati, India

The frenetic pace of urban growth in India has caused major concerns regarding the quality of urban livability. Thus, constructing livable cities has become a major goal for new urbanization in India. But urban livability as a behavioral function of the interaction between urban environment and individual characteristics is still understudied. Therefore, to enhance urban livability and construct people-oriented livable cities, this research study aimed to understand the perception of the residents of Guwahati, India, on urban livability and its determinants. Following the notion of uncertain geographic context problem (UGCoP), the current study developed an appropriate conceptual and methodological framework that evaluated the residents’ satisfaction with urban livability and the effect of its dimensions using statistical methods, which were exploratory factor analysis, structural equation modeling and Spearman’s rank correlation. The empirical results of the study indicate that residents’ mean satisfaction with the city’s livable condition is above dissatisfaction level (2.735) and the four examined dimensions have a positive influence upon residents’ satisfaction with urban livability. Additionally, different socio-economic attributes also exert significant effects on the overall satisfaction with urban livability. Therefore, this study is a practical example and model reference for enhancing urban livability in India, particularly for fast-growing cities.

Household Food Shopping Locations Beyond Residential Neighborhoods: An Exploratory Study Using a GPS-Based Household Survey

Understanding household food shopping locations is important as it could help policymakers develop effective place-based strategies to improve healthy eating and subsequently improve health conditions. Although a growing number of studies have examined food shopping patterns and their associations with food environment around home locations, evidence on food shopping locations relative to areas beyond residential neighborhoods remains scarce. We aimed to explore spatial dimensions of household food shopping locations relative to home, workplaces, and commuting routes. We defined three buffers to delineate potential food shopping geographies for home, workplaces, and commuting routes. We calculated home–grocery distances and work–grocery distances, and the percentage of grocery shopping locations falling inside each of the three aforementioned spatial units, respectively. Our findings suggest that people were more likely to visit food stores beyond immediate residential areas or workplace areas. They often bypassed the closest supermarkets near home or work, whereas they were more likely to shop along their commuting paths. Our findings provide an example of the uncertain geographic context problem in a context of food activities, highlighting that activity space is a more comprehensive measure for assessing food exposures and food shopping activities. Key Words: activity spaces, food shopping locations, healthy food environment, residential neighborhoods, workplace neighborhoods.

Do spatiotemporal units matter for exploring the microgeographies of epidemics?

From the onset of the COVID-19 pandemic in 2020, studies on the microgeographies of epidemics have surged. However, studies have neglected the significant impact of multiple spatiotemporal units, such as report timestamps and spatial scales. This study examines three cities with localized COVID-19 resurgence after the first wave of the pandemic in mainland China to estimate the differential impact of spatiotemporal unit on exploring the influencing factors of epidemic spread at the microscale. The quantitative analysis results suggest that future spatial epidemiology research should give greater attention to the “symptom onset” timestamp instead of only the “confirmed” data and that “spatial transmission” should not be confused with “spatial sprawling” of epidemics, which can greatly reduce comparability between epidemiology studies. This research also highlights the importance of considering the modifiable areal unit problem (MAUP) and the uncertain geographic context problem (UGCoP) in future studies.

The impacts of the built environment on bicycle-metro transfer trips: A new method to delineate metro catchment area based on people's actual cycling space

Bicycle-metro integration is an efficient method of solving the “last mile” issue around metro stations. Built environment is believed to have a significant effect on cycling behavior. However, transfer cycling around metro stations, as a specific type of cycling behavior, has often been overlooked in transport research. In addition, static contextual units such as circular or street-network buffers are typically used to delineate metro catchment areas of transfer cycling trips. These methods are inaccurate to represent the actual geographic contexts of cycling trips, according to the uncertain geographic context problem (UGCoP). Thus, in this study, bicycle-metro catchment areas are delineated based on aggregating the end points of over three million transfer cycling trips. The impact of the built environment on transfer cycling behavior is also explored.First, we find that the aggregate-points buffer outperforms traditional static buffers in predicting transfer cycling trips. Second, we also identify a high level of spatial heterogeneity in catchment area and transfer cycling density between urban and suburban areas. Third, residential and working population density, bus stop density, and metro stations accessibility have a significant effect on bicycle-metro transfer cycling.

Uncertainties in the Assessment of COVID-19 Risk: A Study of People’s Exposure to High-Risk Environments Using Individual-Level Activity Data

Based on different conceptualizations and measures of individual-level environmental exposure, this study examines how the uncertain geographic context problem (UGCoP) and the neighborhood effect averaging problem (NEAP) might affect the assessment of COVID-19 risk. Using the COVID-19 data on an open-access government Web site and the individual-level activity data of sixty confirmed COVID-19 cases (infected persons) in Hong Kong, we first represent COVID-19 risk environments using case-based and venues-based high-risk locations. The COVID-19 risk of each of the sixty selected cases is then evaluated by three approaches based on their exposures to the case-based or venues-based risk environments: the mobility-based approach, the residence-based approach, and the activity space–based approach. The results indicate that the UGCoP and the NEAP exist in the assessment of COVID-19 risk, which has significant implications: Ecological COVID-19 studies need to address the uncertainties due to the UGCoP and the NEAP by considering people’s daily mobility. Otherwise, ignoring peoples’ daily mobility and its interactions with complex and dynamic COVID-19 risk environments could lead to misleading results and misinform government nonpharmaceutical intervention measures.

Interpretation of contextual influences with explanatory tools: Travel mode likelihood mapping using GPS trajectories

AbstractPast studies have failed to address the spatially and temporally varying impacts of environmental factors regarding the uncertain geographic context problem. This study seeks to provide an innovative framework to facilitate the understanding of spatially and temporally varying impacts of multiple contexts on individuals' travel modes using GIS and machine learning techniques. It adopts machine learning techniques to create likelihood maps to predict the spatiotemporal patterns of individual travel behaviors and uses explanatory tools to explore the spatially and temporally varying impacts. The most notable change at a local level in the spatial dimension was that assaults and offenses involving children turned out to be important in two selected communities in Chicago. Regarding the temporally varying impact, batteries, other offenses, and robberies showed negative associations with the walking prediction to some extent at the afternoon peak (5–7:59 p.m.) during weekdays. The proposed approach will enable meaningful interpretation of complex interactions between multiple environmental factors and individual travel behaviors to suggest policies in urban planning and design.

The impact of immediate urban environments on people’s momentary happiness

The research interest of urban researchers and geographers in the relationship between urban environments and happiness has been increasing. Previous studies have mostly focused on people’s long-term overall wellbeing. However, there is limited evidence that momentary happiness is associated with immediate urban environments. This study provides new evidence on this issue. 144 participants living in Guangzhou, China, were asked to repeatedly self-report their momentary happiness through ecological momentary assessment (EMA) and the day reconstruction method (DRM). The microenvironment variables were captured by portable sensors, while the built environment variables were captured by associating the GPS response locations with objective spatial data. The results indicate that momentary happiness is influenced by immediate microenvironment variables and built environment characteristics including temperature, noise, PM2.5, population, POI density, POI types and street intersections. On the other hand, the use of different sizes of contextual units affects the results. The built environment in 100 m buffers and the microenvironment has higher explanatory power for momentary happiness recorded by EMA than the built environment in 500 m buffers. Similarly, the temporality of the contextual influences also affects the results. Urban environment features have higher explanatory power for real-time momentary happiness recorded by EMA than recalled momentary happiness recorded by DRM. These results also strongly corroborate the results of recent studies on the uncertain geographic context problem (UGCoP) and partly explain the inconsistency in the results of past research.

The Effects of GPS-Based Buffer Size on the Association between Travel Modes and Environmental Contexts

To investigate the association between physical activity (including active travel modes) and environmental factors, much research has estimated contextual influences based on zones or areas delineated with buffer analysis. However, few studies to date have examined the effects of different buffer sizes on estimates of individuals’ dynamic exposures along their daily trips recorded as GPS trajectories. Thus, using a 7-day GPS dataset collected in the Chicago Regional Household Travel Inventory (CRHTI) Survey, this study addresses the methodological issue of how the associations between environmental contexts and active travel modes (ATMs) as a subset of physical activity vary with GPS-based buffer size. The results indicate that buffer size influences such associations and the significance levels of the seven environmental factors selected as predictors. Further, the findings on the effects of buffer size on such associations and the significance levels are clearly different between the ATMs of walking and biking. Such evidence of the existence of buffer-size effects for multiple environmental factors not only confirms the importance of the uncertain geographic context problem (UGCoP) but provides a resounding cautionary note to all future research on human mobility involving individuals’ GPS trajectories, including studies on physical activity and travel behaviors, especially on the reliable estimation of individual exposures to environmental factors and their health outcomes.

Assessing Mobility-Based Real-Time Air Pollution Exposure in Space and Time Using Smart Sensors and GPS Trajectories in Beijing

Using real-time data from portable air pollutant sensors and smartphone Global Positioning System trajectories collected in Beijing, China, this study demonstrates how smart technologies and individual activity-travel microenvironments affect the assessment of individual-level pollution exposure in space and time at a very fine resolution. It compares three different types of individual-level exposure estimates generated by using residence-based monitoring station assessment, mobility-based monitoring station assessment, and mobility-based real-time assessment. Further, it examines the differences in personal exposure to PM2.5 associated with different activity places and travel modes across various environmental conditions. The results show that the exposure estimates generated by monitoring station assessment and real-time sensing assessment vary substantially across different activity locations and travel modes. Individual-level daily exposure for residents living in the same community also varies significantly, and there are substantial differences in exposure levels using different approaches. These results indicate that residence- or mobility-based monitoring station assessments, which cannot account for the differences in air pollutant exposures between outdoor and indoor environments and between different travel-related microenvironments, could generate considerably biased estimates of personal pollution exposure. Key Words: indoor environment, real-time exposure to air pollution, smart technologies, travel modes, the uncertain geographic context problem.

Beyond Commuting: Ignoring Individuals' Activity-Travel Patterns May Lead to Inaccurate Assessments of Their Exposure to Traffic Congestion.

This research examines whether individual exposures to traffic congestion are significantly different between assessments obtained with and without considering individuals’ activity-travel patterns in addition to commuting trips. We used crowdsourced real-time traffic congestion data and the activity-travel data of 250 individuals in Los Angeles to compare these two assessments of individual exposures to traffic congestion. The results revealed that individual exposures to traffic congestion are significantly underestimated when their activity-travel patterns are ignored, which has been postulated as a manifestation of the uncertain geographic context problem (UGCoP). The results also highlighted that the probability distribution function of exposures is heavily skewed but tends to converge to its average when individuals’ activity-travel patterns are considered when compared to one obtained when those patterns are not considered, which indicates the existence of the neighborhood effect averaging problem (NEAP). Lastly, space-time visualizations of individual exposures illustrated that people’s exposures to traffic congestion vary significantly even if they live at the same residential location due to their idiosyncratic activity-travel patterns. The results corroborate the claims in previous studies that using data aggregated over areas (e.g., census tracts) or focusing only on commuting trips (and thus ignoring individuals’ activity-travel patterns) may lead to erroneous assessments of individual exposures to traffic congestion or other environmental influences.

An Analytical Framework for Integrating the Spatiotemporal Dynamics of Environmental Context and Individual Mobility in Exposure Assessment: A Study on the Relationship between Food Environment Exposures and Body Weight.

In past studies, individual environmental exposures were largely measured in a static manner. In this study, we develop and implement an analytical framework that dynamically represents environmental context (the environmental context cube) and effectively integrates individual daily movement (individual space-time tunnel) for accurately deriving individual environmental exposures (the environmental context exposure index). The framework is applied to examine the relationship between food environment exposures and the overweight status of 46 participants using data collected with global positioning systems (GPS) in Columbus, Ohio, and binary logistic regression models. The results indicate that the proposed framework generates more reliable measurements of individual food environment exposures when compared to other widely used methods. Taking into account the complex spatial and temporal dynamics of individual environmental exposures, the proposed framework also helps to mitigate the uncertain geographic context problem (UGCoP). It can be used in other environmental health studies concerning environmental influences on a wide range of health behaviors and outcomes.

County Socioeconomic Deprivation and Preterm Birth Risk Between White and Black Mothers in Georgia, USA

Past studies have reported the association between living in a socioeconomically deprived neighborhood and elevated preterm births (PtB) risk after adjusting certain individual level confounders. This article examined the association between county-level deprivation and PtB risk of three stratified racial groups, white, black, and the others. The author built two level random intercept logistic regression models using 112,589 single live births retrieved from vital statistics record in Georgia, USA in 2010. Although county level deprivation was found to be insignificant for PtB risk for the entire study population, it had a significant yet modest effect on magnifying the PtB risk of black women (The odds ratio (OR) = 1.063,95% CI = 1.02, 1.12). In addition, the Median Odds Ratio (MOR) (1.229) indicated a weak neighborhood effect on PtB risk and the Interval Odds Ratio (IOR)-80% (0.68-1.49) suggested large unaccounted county-level heterogeneity. Future research will include more confounders at both levels in analysis as well as addressing the uncertain geographic context problem (UGCoP).

Environmental Influences on Leisure-Time Physical Inactivity in the U.S.: An Exploration of Spatial Non-Stationarity

Considerable research has been conducted to advance our understanding of how environmental factors influence people’s health behaviors (e.g., leisure-time physical inactivity) at the neighborhood level. However, different environmental factors may operate differently at different geographic locations. This study explores the inconsistent findings regarding the associations between environmental exposures and physical inactivity. To address spatial autocorrelation and explore the impact of spatial non-stationarity on research results which may lead to biased estimators, this study uses spatial regression models to examine the associations between leisure-time physical inactivity and different social and physical environmental factors for all counties in the conterminous U.S. By comparing the results with the conventional ordinary least squares regression and spatial lag model, the geographically weighted regression model adequately addresses the problem of spatial autocorrelation (Moran’s I of the residual = 0.0293) and highlights the spatial non-stationarity of the associations. The existence of spatial non-stationarity that leads to biased estimators, which were often ignored in past research, may be another reason for the inconsistent findings in previous studies besides the modifiable areal unit problem and the uncertain geographic context problem. Also, the observed associations between environmental variables and leisure-time physical inactivity are helpful for developing location-based policies and interventions to encourage people to undertake more physical activity.