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Inequalities in accessibility to grocery stores can lead to disparate health outcomes among the population. Although existing studies have examined grocery accessibility inequality across income and racial/ethnic groups, little research has been dedicated to revealing the intra‐racial disparities of grocery accessibility and comparing inter‐racial and intra‐racial inequalities in grocery accessibility. This study adopts a modified two‐step floating catchment area (2SFCA) method, which accounts for the supply/demand inflation effect, and an alternative distribution inequality metric called the Palma ratio to measure the inequalities in grocery access between the richest 10% and poorest 40% of the census tracts within the same racial/ethnic group in Chicago (USA). The results indicate that in Chicago, inter‐racial inequality in grocery accessibility is more serious than intra‐racial inequality, especially because the Hispanic‐majority census tracts generally suffer from low grocery accessibility regardless of their income levels. [ABSTRACT FROM AUTHOR]

This study examines the job-access inequity between the richest 10% and poorest 40% transit-based workers across space (i.e. central city, the inner-ring/outer-ring suburb) and race (i.e. white, black and Hispanic) in Chicago. The results indicate that there are job-access inequities across both space and race. In terms of job-access inequity across race, there are more job-access inequities for whites and blacks than for Hispanics. In terms of job-access inequity across space, the central city has the least cross-race inequities while the outer-ring suburb has the most cross-race inequities. Overall, job-access inequities are more serious across space than across race. [ABSTRACT FROM AUTHOR]

Public transit is indispensable for car‐free households to access healthcare. Meanwhile, different households have unequal transit‐based healthcare accessibility due to different socio‐economic factors such as race/ethnicity and car ownership. Few studies have comprehensively explored the inequality in transit‐based healthcare accessibility by integrating both racial/ethnic inequity and car ownership. This study fills the gap with an up‐to‐date analysis of transit‐based healthcare accessibility in the Chicago Metropolitan Area at the census tract level. The results show that the percentage of car‐free households is positively related to transit‐based healthcare accessibility; while a higher percentage of minorities (i.e., Black/Hispanic) is negatively related to transit‐based healthcare accessibility. Among all neighbourhoods with higher percentages of higher‐than‐average car‐free households, Hispanic‐majority neighbourhoods fare the worst; while White‐majority neighbourhoods have much better healthcare accessibility than both Black‐majority and Hispanic‐majority neighbourhoods. Key Messages: White‐ and Hispanic‐majority neighbourhoods with higher‐than‐average percentages of car‐free households have the highest and lowest levels of healthcare accessibility respectively.The percentage of car‐free households and minorities are positively and negatively related to the healthcare accessibility score respectively.The racial/ethnic factor has a stronger association with healthcare accessibility outcome than car ownership status. [ABSTRACT FROM AUTHOR]

Past 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. [ABSTRACT FROM AUTHOR]

An integrated analysis of housing and transport affordability provides comprehensive insights into the affordability of different locations in a city. By focusing on transit‐based workers, who constitute a significant portion of commuters but are understudied in the affordability literature, this study proposes a new, integrated method for estimating housing and transit (H + T) affordability in the Chicago Metropolitan Area using census data and data from the Google Maps Directions API. Methodologically, the study contributes to the literature by proposing and implementing a new method that estimates H + T affordability in an integrated manner based on census data and data from the Google Maps Directions API across three types of housing occupants (i.e., owners with/without a mortgage and renters). Empirically, the results indicate varying H + T affordability distribution patterns among different types of housing occupants and provide a comprehensive picture of housing and transport affordability in the study area without incurring prohibitive time and cost of data collection. The study indicates that policy‐makers should consider expanding the existing concessionary fare scheme to include low‐income residents and establishing affordable housing programmes that subsidise not only mortgage/rent but also miscellaneous costs related to housing (such as utilities) in order to improve the overall H + T affordability for different types of housing occupants. [ABSTRACT FROM AUTHOR]

Job accessibility, as a crucial social equity indicator, has been primarily measured by travel time/distance and transit fare. However, the effect of income has rarely been integrated with the total travel monetary cost (i.e. the sum of transit fare and travel time monetary cost) in measuring job accessibility. The different sensitivity to travel cost of people with different income levels should be included in job accessibility measures. This study proposes a measure that turns travel time, transit fare and income into a comparable unit and considers the total travel monetary cost as a percentage of income. We measure transit‐based accessibility to low‐pay jobs in Chicago before and after considering the total travel monetary cost as a percentage of income. The results indicate that the job accessibility for low‐income people is overestimated while the job accessibility for non‐low‐income people is underestimated based on the traditional measure. The proposed measure mitigates the overestimation and underestimation effects and helps better inform policy‐makers of the real job accessibility distribution pattern. [ABSTRACT FROM AUTHOR]

Accompanied by higher levels of car ownership, suburbanisation in Western countries has a significant impact on motorised travel and long-distance trips. Chinese cities share some of the suburbanisation trends and patterns of the United States as well as an increasing number of cars, while there are still great contextual variations. Through a comparative study of activity space in two inner suburban areas with different built environments, Beijing in China and Chicago in the US, this paper examines how suburbanisation influences individuals’ activity-travel behaviour. The results indicate that changing urban form has a significant impact on activity space. However, the determinants of activity space in Beijing are quite different from those in Chicago. In Beijing, increasing car ownership leads to more differentiation among suburban residents. In Chicago, encouraging high-density land use patterns may lead to more localised activity space. [ABSTRACT FROM AUTHOR]

Spatial flow outlier (SFO) detection aims to discover spatial flows whose non-spatial attribute values are significantly different from their neighborhoods. Different from spatial flow clusters, which are the main concern in the current literature, SFOs represent unusual local instabilities and are valuable for revealing anomalous spatial interactions between regions. Detecting SFOs is challenging because the underlying distribution of the flow data is unknown a priori, and inappropriate distribution assumptions may lead to misleading decisions on SFOs. Surprisingly, spatial autocorrelation, which is a common property of geographic data, has not been considered in the null hypothesis for testing spatial outliers. To solve this significant methodological issue, we propose a spatial-autocorrelation-aware detection method. This method detects SFOs by testing the local difference of attribute values in flow neighborhoods against the null hypothesis that neighboring flows are similar. To construct this null hypothesis, we develop a distribution-free model by reconstructing the observed spatial autocorrelation. Synthetic experiments and a case study using the journey-to-work flow data in Chicago demonstrate that the choice and modeling of the null hypothesis has a significant influence on the statistical inference of SFOs. By taking the inherent spatial autocorrelation into account, our method can more objectively assess the significance of SFOs than two baseline methods based on the normality and randomization hypotheses. • This study proposes a spatial-autocorrelation-aware detection method to discover spatial flow outliers (SFOs). • It formulates a novel interpretation of SFOs by testing against the null hypothesis H 0 of spatial autocorrelation. • Using the commuting flow data in Chicago, it shows that the H 0 and its modelingsignificantly affect the inference of SFOs. • The proposed method can more objectively assess the significance of SFOs than normality- and randomization-based methods. [ABSTRACT FROM AUTHOR]

The effects of environmental exposure on human health have been widely explored by scholars in health geography for decades. However, recent advances in geospatial technologies, especially the development of mobile approaches to collecting real-time and high-resolution individual data, have enabled sophisticated methods for assessing people's environmental exposure. This study proposes an individual environmental exposure assessment system (IEEAS) that integrates objective real-time monitoring devices and subjective sensing tools to provide a composite way for individual-based environmental exposure data collection. With field test data collected in Chicago and Beijing, we illustrate and discuss the advantages of the proposed IEEAS and the composite analysis that could be applied. Data collected with the proposed IEEAS yield relatively accurate measurements of individual exposure in a composite way, and offer new opportunities for developing more sophisticated ways to measure individual environmental exposure. With the capability to consider both the variations in environmental risks and human mobility in high spatial and temporal resolutions, the IEEAS also helps mitigate some uncertainties in environmental exposure assessment and thus enables a better understanding of the relationship between individual environmental exposure and health outcomes. [ABSTRACT FROM AUTHOR]

Urban green space (UGS) has positive impacts on people's physical and mental health. Equal access to UGS for all people, regardless of their individual characteristics, is key to the achievement of better public health outcomes. Existing studies have focused largely on inequity in spatial UGS accessibility distribution but failed to uncover the disparities of UGS accessibility among different racial/ethnic and income groups as well as inequity in income-based UGS accessibility distribution within the same racial/ethnic groups. By using the city of Chicago as the study area, we adopt the two-step floating catchment area (2SFCA) method to measure census tract-level UGS accessibility and compare distribution disparities among white-majority, black-majority and Hispanic-majority census tracts as well as low-income and non-low-income census tracts. In addition, we employ the Palma ratio to measure the income-based UGS accessibility distribution inequity within respective racial/ethnic groups. The results show that white-majority census tracts generally enjoy significantly better UGS accessibility than minority-dominated census tracts, while black-majority census tracts have higher UGS accessibility than Hispanic-majority census tracts. In terms of the intra-racial/ethnic income-based UGS accessibility inequity between the richest and the poorest, inequity is the lowest among white-majority census tracts and the highest among black-majority census tracts, with inequity in Hispanic-majority census tracts in the intermediate range. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic has had tremendous and extensive impacts on the people's daily activities. In Chicago, the numbers of crime fell considerably. This work aims to investigate the impacts that COVID-19 has had on the spatial and temporal patterns of crime in Chicago through spatial and temporal crime analyses approaches. The Seasonal-Trend decomposition procedure based on Loess (STL) was used to identify the temporal trends of different crimes, detect the outliers of crime events, and examine the periodic variations of crime distributions. The results showed a certain phase pattern in the trend components of assault, battery, fraud, and theft. The largest outlier occurred on 31 May 2020 in the remainder components of burglary, criminal damage, and robbery. The spatial point pattern test (SPPT) was used to detect the similarity between the spatial distribution patterns of crime in 2020 and those in 2019, 2018, 2017, and 2016, and to analyze the local changes in crime on a micro scale. It was found that the distributions of crime significantly changed in 2020 and local changes in theft, battery, burglary, and fraud displayed an aggregative cluster downtown. The results all claim that spatial and temporal patterns of crime changed significantly affected by COVID-19 in Chicago, and they offer constructive suggestions for local police departments or authorities to allocate their available resources in response to crime. [ABSTRACT FROM AUTHOR]