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Developing walkable urban environments is crucial for enhancing urban liveability and sustainability. To evaluate the chrono‐urbanism status, this study combines conventional census and mapping data with social media check‐in big data. A composite index is proposed, which assesses the accessibility of essential urban functions under the 5/10/15‐minute chrono‐thresholds. This index incorporates five categories of urban functions (public transit, living, education, health care and entertainment) in Macau. Utilising check‐in information from Sina Weibo, the level of attraction for different urban functions is integrated into the calculation. The findings highlight significant disparities in chrono‐urbanism status, with regions inhabited by lower socio‐economic status residents being more vulnerable than the affluent institutional and commercial centre of Macau. Leveraging social media big data, this study provides valuable insights to urban planners and policy‐makers, enabling them to understand the overall chrono‐urbanism status and target interventions for developing a more sustainable and liveable urban environment in Macau. [ABSTRACT FROM AUTHOR]

Community roads are crucial for efficient navigation in residential areas. However, current navigation maps often lack comprehensive coverage of these roads. To address this issue, we present a Human-Flow Model (HFM) to identify roads within residential communities by utilizing abundant low-frequency trajectory data from human movements. First, the HFM leverages human movement density in residential zones to estimate the likelihood of road existence. Using the probability distribution of human movement and neighboring building footprints, we construct a Human-Flow Probability Field (HFPF), which serves as a distribution representation for modeling human movement within densely populated built-up areas. Then, the flow paths are extracted from the HFPF using hydrological analysis techniques, which facilitates the identification of main paths and smaller branches within the community road network. Finally, the road network is refined using morphological methods. Our model was tested using six residential communities located in Wuhan, China. It consistently outperformed other methods by detecting more roads with higher accuracy, especially intricate branches. By incorporating flow semantics, our model capitalizes on sparse trajectory data to enhance the fine-scale community road networks. This improvement enhances the last-mile navigation experience in sustainable cities, contributing to overall urban mobility and convenience for residents. [ABSTRACT FROM AUTHOR]

Copyright of Annals of the American Association of Geographers is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study advances the measurement of community social context by introducing the daily dynamic perspective to promote a better understanding of the relationship between community social context and community attachment. It measured the social context averaging or polarization (SCAP) effect of communities every 3 h using census and cell phone data and investigated residents' community attachment in 71 communities in Guangzhou, China. There are three findings. First, the social contexts of many communities varied during the day, either moving toward the mean of the whole city or away from the mean. Distinct patterns exist during work hours, evening hours, and night hours. Second, considering variations in social contexts during the evening hours significantly enhances the explanation of the heterogeneity in residents' community attachment. Third, variations in social contexts are more likely to influence residents' attachment in old blocks and migrant communities, and communities that may have gated sub-units are less likely to be influenced. The study advocates that context dynamics be taken as a new dimension of community indicators in place perception studies. The study is also instructive in targeting space and time that deserve special attention in community governance practice. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

It is common to observe the epidemic risk perception (ERP) and a decline in subjective well-being (SWB) in the context of public health events, such as Corona Virus Disease 2019 (COVID-19). However, there have been few studies exploring the impact of individuals' ERP within living space on their SWB, especially from a geographical and daily activity perspective after the resumption of work and other activities following a wave of the pandemic. In this paper, we conducted a study with 789 participants in urban China, measuring their ERP within living space and examining its influence on their SWB using path analysis. The results indicated that individuals' ERP within their living space had a significant negative effect on their SWB. The density of certain types of facilities within their living space, such as bus stops, subway stations, restaurants, fast food shops, convenience shops, hospitals, and public toilets, had a significantly negative impact on their SWB, mediated by their ERP within living space. Additionally, participation in out-of-home work and other activities not only increased individuals' ERP within living space, but also strengthened its negative effect on their SWB. [ABSTRACT FROM AUTHOR]

Air pollution poses a threat to human health. Public perceptions of air pollution are important for individual self-protection and policy-making. Given the uncertainty faced by residence-based exposure (RB) measurements, this study measures individuals' real-time mobility-based (MB) exposures and perceptions of air pollution by considering people's daily movement. It explores how contextual uncertainties may influence the disparities in perceived air quality by taking into account RB and MB environmental factors. In addition, we explore factors that are related to the mismatch between people's perceived air quality and actual air pollution exposure. Using K-means clustering to divide the PM2.5 values into two groups, a mismatch happens when the perceived air quality is poor but the air pollution level is lower than 15.536μg/m3 and when the perceived air quality is good but the air pollution level is higher than 15.608μg/m3. The results show that there is a mismatch between air pollution exposure and perception of air pollution. People with low income are exposed to higher air pollution. Unemployed people and people with more serious mental health symptoms (e.g., depression) have a higher chance of accurately assessing air pollution (e.g., perceiving air quality as poor when air pollution levels are high). Older people and those with a higher MB open space density tend to underestimate air pollution. Students tend to perceive air quality as good. People who are surrounded by higher MB transportation land-use density and green space density tend to perceive air quality as poor. The results can help policymakers to increase public awareness of high air pollution areas, and consider the health effects of landscapes during planning. [ABSTRACT FROM AUTHOR]

The reduction of social interactions through non-pharmaceutical interventions (NPIs) has been shown to effectively curb COVID-19 transmission. However, these control measures were often accompanied by changes in people's daily routines and constraints on their activity space, which could lead to mental distress (i.e., anxiety and depression). This study examined the interplay among individuals' anxiety, depression, daily activities, and perceptions of COVID-19 and neighborhood cohesion. Taking Hong Kong as an example, an online survey (N = 376) was conducted to collect data from participants between March 14 to May 11, 2022. The data include respondents' self-reported anxiety and depressive symptoms, daily activities (e.g., smartphone use), perceptions of COVID-19 (e.g., the possibility of infecting COVID-19), and perceptions of neighborhood cohesion. Using network analysis, we found that excessive smartphone use, life disturbance by COVID-19, and a community with people getting along well with each other were significant factors associated with participants' anxiety and depression. Using critical path analysis, we observed that NPIs reduced human mobility, led to delayed bedtime, and increased smartphone use, which were associated with participants' mental distress. We also found that NPIs and COVID-19 were associated with people's perceptions of infection and the severity of COVID-19 and human mobility flexibility, which may further lead to mental distress. Our results also demonstrated that people with high education levels were vulnerable. These results provided important insights for designing appropriate interventions without generating deleterious impacts on people's mental health in the future. [ABSTRACT FROM AUTHOR]

Background: As the COVID-19 pandemic became a major global health crisis, many COVID-19 control measures that use individual-level georeferenced data (e.g., the locations of people's residences and activities) have been used in different countries around the world. Because these measures involve some disclosure risk and have the potential for privacy violations, people's concerns for geoprivacy (locational privacy) have recently heightened as a result, leading to an urgent need to understand and address the geoprivacy issues associated with COVID-19 control measures that use data on people's private locations. Methods: We conducted an international cross-sectional survey in six study areas (n = 4260) to examine how people's political views, perceived social norms, and individualism shape their privacy concerns, perceived social benefits, and acceptance of ten COVID-19 control measures that use individual-level georeferenced data. Multilevel linear regression models were used to examine these effects. We also applied multilevel structure equation models (SEMs) to explore the direct, indirect, and mediating effects among the variables. Results: We observed a tradeoff relationship between people's privacy concerns and the acceptance (and perceived social benefits) of the control measures. People's perceived social tightness and vertical individualism are positively associated with their acceptance and perceived social benefits of the control measures, while horizontal individualism has a negative association. Further, people with conservative political views and high levels of individualism (both vertical and horizontal) have high levels of privacy concerns. Conclusions: Our results first suggest that people's privacy concerns significantly affect their perceived social benefits and acceptance of the COVID-19 control measures. Besides, our results also imply that strengthening social norms may increase people's acceptance and perceived social benefits of the control measures but may not reduce people's privacy concerns, which could be an obstacle to the implementation of similar control measures during future pandemics. Lastly, people's privacy concerns tend to increase with their conservatism and individualism. [ABSTRACT FROM AUTHOR]

With the growing concern for urban residents' living environment, neighbourhood life circle research has drawn increasing attention from urban planners and geographers. The neighbourhood life circle refers to the spatial range of people's daily activities around their residential neighbourhoods and the city facilities in such an area for meeting their needs. It has been used as a new type of planning unit in people-oriented urban planning. Therefore, how to delineate the neighbourhood life circle has become one of the primary topics of neighbourhood life circle research and planning. Despite several existing methods for neighbourhood life circle delineation, they ignored people's actual daily activities. Further, few studies have examined the internal spatial utilization of neighbourhood life circles. To fill the gaps, this study proposes an innovative context-based approach for neighbourhood life circle delineation, which takes accessibility and walkability into account. It also presents an analytical framework for examining the internal spatial utilization and shared spaces of neighbourhood life circles based on people's GPS tracking data. With a case study of three neighbourhoods in Beijing, China, the proposed approach successfully identifies the context-based neighbourhood life circles as well as their internal spatial utilization and shared spaces. The findings provide a practical way for identifying the spatial scope of neighbourhood life circles and understanding their internal spatial utilization and neighbourhood spatial sharing relationships. The proposed method will also serve as a fundamental method for neighbourhood life circle research and shed light on urban planning practices. [ABSTRACT FROM AUTHOR]

In practice, the ride-hailing platform generally provides two products: express services and ridesplitting services, and driver resources are usually shared across these two products on the supply side, i.e., hybrid ride-hailing mode. This study tackles the order dispatch problem with considering flexible one-to-three matching strategy under hybrid modes. In this problem, if a driver serves express requests, only one order can be matched with; if a driver serves ridesplitting requests, this driver can be matched with at most three orders simultaneously or sequentially. Considering one-to-three matching, en-route matching, and multiple passengers per order, this problem is formulated as a multi-objective integer programming, a hierarchical approach is adopted to address it. Using the ride-hailing trip data, we examine characteristics of this problem, such as the impact of service priority and the benefits of one-to-three matching. Finally, some beneficial management implications are proposed to improve the operation of the ride-hailing service. [ABSTRACT FROM AUTHOR]

Urban buildings are an integral component of urban space, and accurately identifying their spatial configurations and grouping them is vital for various urban applications. However, most existing building clustering methods only utilize the original spatial and nonspatial features of buildings, disregarding the potential value of complementary information from multiple perspectives. This limitation hinders their effectiveness in scenarios with intricate spatial configurations. To address this, this article proposes a novel multi‐view building clustering method that captures cross‐view information from spatial and nonspatial features. Drawing inspiration from both spatial proximity characteristics and nonspatial attributes, three views are established, including two spatial distance graphs (centroid distance graph and the nearest outlier distance graph) and a building attribute graph (multiple‐attribute graph). The three graphs undergo iterative cross‐diffusion processes to amplify similarities within each predefined graph view, culminating in their fusion into a unified graph. This fusion facilitates the comprehensive correlation and mutual enhancement of spatial and nonspatial information. Experiments were conducted using 10 real‐world community‐building datasets from Wuhan and Chengdu, China. The results demonstrate that our approach achieves 21.27% higher accuracy and 22.28% higher adjusted rand index in recognizing diverse complex arrangements compared to existing methods. These findings highlight the importance of leveraging complementary and consensus information across different feature dimensions for improving the performance of building clustering. [ABSTRACT FROM AUTHOR]

Causal inference in complex systems has been largely promoted by the proposal of some advanced temporal causation models. However, temporal models have serious limitations when time series data are not available or present insignificant variations, which causes a common challenge for earth system science. Meanwhile, there are few spatial causation models for fully exploring the rich spatial cross-sectional data in Earth systems. The generalized embedding theorem proves that observations can be combined together to construct the state space of the dynamic system, and if two variables are from the same dynamic system, they are causally linked. Inspired by this, here we show a Geographical Convergent Cross Mapping (GCCM) model for spatial causal inference with spatial cross-sectional data-based cross-mapping prediction in reconstructed state space. Three typical cases, where clearly existing causations cannot be measured through temporal models, demonstrate that GCCM could detect weak-moderate causations when the correlation is not significant. When the coupling between two variables is significant and strong, GCCM is advantageous in identifying the primary causation direction and better revealing the bidirectional asymmetric causation, overcoming the mirroring effect. Temporal causation models perform poorly in causal inference for variables with limited temporal variations. This paper establishes a causal inference model, which can reveal the nonlinear complex casual associations based on cross-sectional Earth System data. [ABSTRACT FROM AUTHOR]

Copyright of Annals of the American Association of Geographers is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Urbanization has led to environmental challenges, with the urban heat island effect being a prominent concern. Understanding the influence of urban environmental characteristics (UECs) on land surface temperature (LST) is essential for addressing this issue and promoting sustainable urban development. The spatiotemporal characteristics and influencing factors of LST have been investigated in past studies, but research that explicitly investigates the key factors and long-term spatial relationships affecting LST in city-scale urban areas is limited. Remote sensing techniques provide valuable insights into LST patterns and the relationship between urban environment and temperature dynamics. We utilized Landsat 8 images to derive the LST and six spectral indices from 2017 to 2022 in Hong Kong, a city characterized by high population density and rapid urban growth. We also acquired land use data to reflect Hong Kong's dynamic urban landscape. The complex interactions between urban environment and LST were analyzed using various analytical techniques, including slope trend analysis, land use change detection, and correlation analysis. Finally, we constructed a random forest model to assess the importance of each environmental characteristic. Our findings provide three key insights for regions experiencing rapid urbanization. First, the LST showed an increasing trend in Hong Kong from 2017 to 2022, with the annual LST rising from 21.13 °C to 23.46 °C. Second, we identify negative relationships between LST and vegetation (−0.49)/water bodies (−0.49) and a positive correlation between LST and built-up areas (0.56) utilizing Pearson's correlation. Third, the dominant influence of built-up areas was underscored, contributing as much as 53.4% to elevated LST levels, with specific attention to the substantial reclamation activities in Hong Kong. The insights from this study provide valuable guidance for policymakers, urban planners, and environmental researchers to formulate evidence-based strategies to achieve a resilient, livable urban future. [ABSTRACT FROM AUTHOR]

Causal inference in complex systems has been largely promoted by the proposal of some advanced temporal causation models. However, temporal models have serious limitations when time series data are not available or present insignificant variations, which causes a common challenge for earth system science. Meanwhile, there are few spatial causation models for fully exploring the rich spatial cross-sectional data in Earth systems. The generalized embedding theorem proves that observations can be combined together to construct the state space of the dynamic system, and if two variables are from the same dynamic system, they are causally linked. Inspired by this, here we show a Geographical Convergent Cross Mapping (GCCM) model for spatial causal inference with spatial cross-sectional data-based cross-mapping prediction in reconstructed state space. Three typical cases, where clearly existing causations cannot be measured through temporal models, demonstrate that GCCM could detect weak-moderate causations when the correlation is not significant. When the coupling between two variables is significant and strong, GCCM is advantageous in identifying the primary causation direction and better revealing the bidirectional asymmetric causation, overcoming the mirroring effect. Temporal causation models perform poorly in causal inference for variables with limited temporal variations. This paper establishes a causal inference model, which can reveal the nonlinear complex casual associations based on cross-sectional Earth System data. [ABSTRACT FROM AUTHOR]

Distance from competitors is a key factor in retail site selection and profitability. To understand the locational tendency that each newly opened outlet locates close to or far from existing competitors in a target area, a specific method is needed. Hence, this study aims first to develop a statistical method to discover the local spatial associations between newly opened and existing point‐like outlets on a street network. We achieve this objective by extending the network local cross K function. The second objective is to evaluate the practicality of the proposed method by applying it to restaurants in a trendy district in Tokyo. Specifically, this study focuses on answering two questions: first, whether each newly opened restaurant is closely located to existing ones or not and, second, whether each existing restaurant attracts newly opened restaurants or not. The results show that the method is useful for revealing the location tendencies of retail outlets toward competitors. [ABSTRACT FROM AUTHOR]

Background: Non-pharmaceutical interventions (NPIs) implemented in one place can affect neighboring regions by influencing people's behavior. However, existing epidemic models for NPIs evaluation rarely consider such spatial spillover effects, which may lead to a biased assessment of policy effects. Methods: Using the US state-level mobility and policy data from January 6 to August 2, 2020, we develop a quantitative framework that includes both a panel spatial econometric model and an S-SEIR (Spillover-Susceptible-Exposed-Infected-Recovered) model to quantify the spatial spillover effects of NPIs on human mobility and COVID-19 transmission. Results: The spatial spillover effects of NPIs explain 61.2 % [ 95 % credible interval: 52.8- 84.4 % ] of national cumulative confirmed cases, suggesting that the presence of the spillover effect significantly enhances the NPI influence. Simulations based on the S-SEIR model further show that increasing interventions in only a few states with larger intrastate human mobility intensity significantly reduce the cases nationwide. These region-based interventions also can carry over to interstate lockdowns. Conclusions: Our study provides a framework for evaluating and comparing the effectiveness of different intervention strategies conditional on NPI spillovers, and calls for collaboration from different regions. [ABSTRACT FROM AUTHOR]

Good access to greenspace and healthy food has commonly been found to be positively associated with health outcomes, despite some studies finding no significant relationship between them. Examining inequalities in accessing greenspace and healthy food among different disadvantaged neighborhoods can help reveal the disadvantaged races/ethnicities in cities with a high level of residential segregation (i.e., population of the same race/ethnicity concentrated in the same neighborhoods). However, existing studies have mostly focused on measuring the inequalities in accessing either greenspace or healthy food alone, which can lead to the inaccurate depiction of disadvantaged neighborhoods in healthy living environments. Therefore, this paper aims at improving the assessment of doubly disadvantaged neighborhoods by considering accessibility to both greenspace and healthy food in the City of Chicago. Our results show that black-majority neighborhoods are the most doubly disadvantaged in terms of exposure to healthy living environments. This study can help guide policymakers to divert more resources towards the improvement of the urban environment for the most doubly disadvantaged neighborhoods. [ABSTRACT FROM AUTHOR]

In this commentary we reflect on the potential and power of geographical analysis, as a set of methods, theoretical approaches, and perspectives, to increase our understanding of how space and place matter for all. We emphasize key aspects of the field, including accessibility, urban change, and spatial interaction and behavior, providing a high‐level research agenda that indicates a variety of gaps and routes for future research that will not only lead to more equitable and aware solutions to local and global challenges, but also innovative and novel research methods, concepts, and data. We close with a set of representation and inclusion challenges to our discipline, researchers, and publication outlets. [ABSTRACT FROM AUTHOR]

The effects of artificial light at night (ALAN) on human health have drawn increased attention in the last two decades. Numerous studies have discussed the effects of ALAN on human health on diverse topics. A broader scope of how ALAN may affect human health is thus urgently needed. This paper depicts a systematic evidence map in a multi-component framework to link ALAN with human health through a comprehensive literature review of English research articles in the past two decades. A three-phase systematic review was conducted after a generalized search of relevant articles from three publication databases, namely Scopus, the Web of Science, and PubMed. In total, 552 research articles were found in four categories and on numerous topics within our framework. We cataloged the evidence that shows direct and indirect as well as positive and negative effects of ALAN on human physical and mental health. We also summarized the studies that consider ALAN as a social determinant of human health. Based on our framework and the systematic evidence map, we also suggest several promising directions for future studies, including method design, co-exposure and exposome studies, and social and environmental justice. [ABSTRACT FROM AUTHOR]

Land subsidence, a common geological phenomenon in deltaic regions, poses significant risks to infrastructures, environments, and human lives. Monitoring and understanding land subsidence are crucial for establishing resilient, adaptive, and sustainable environments. In this study, a robust multi-temporal interferometric synthetic aperture radar (MTInSAR) method was employed to monitor land subsidence in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) using 541 Sentinel-1 SAR images. Spatial auto- and cross-correlation analysis were applied to select the most reasonable explanatory variables from nine candidates in each city of the GBA. City-level geographically weighted regression (GWR) models were then constructed to explore the spatially nonstationary effects of natural and human-induced factors. The findings revealed subsidence velocities ranging from 0 to 83.7 mm/yr in the GBA from 2015 to 2021, with the dominant factors affecting subsidence varying among regions. Natural and human-related factors accounted for 51.13% and 48.87%, respectively. Further analysis of representative subsidence areas highlights the importance of continuous monitoring of ground deformation using MTInSAR, region-specific land subsidence mitigation strategies, and regular maintenance of urban infrastructure. These insights are valuable for policymakers and urban planners in comprehending the complex processes underlying land subsidence and informing decision-making processes for sustainable and resilient urban development. [ABSTRACT FROM AUTHOR]

Exposure to green-blue space has been shown to be associated with better physical and mental health outcomes. The advent of COVID-19 has underlined the importance for people to have access to green-blue spaces in proximity to their residences due to pandemic-related restrictions on activity space. The implementation of the 15-min concept, which advocates that people should be able to reach locations of essential functions like green-blue spaces within 15 min of active travel, can bring green-blue spaces nearer to where people live. Nonetheless, there is still a lack of understanding of the social and spatial (in)equality in 15-min green-blue space accessibility by active travel in cities seeking to embrace the concept, such as Hong Kong. This study explores 15-min green-blue space accessibility by walking and cycling in Hong Kong to reveal the distribution of disadvantaged neighborhoods. The results show that neighborhoods in Kowloon's districts are the most disadvantaged in accessing green-blue spaces within 15 min of active travel. Our study provides policymakers with valuable insights and knowledge conducive to formulating policies aimed at reducing inequality in 15-min accessibility. [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]

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]

Geospatial models can facilitate the delineation of food access patterns, which is particularly relevant for urban planning and health policymaking. Because community food environmental studies use different analysis units or study scales, the rigor and consistency of their evaluations cannot be ensured. This issue is known as the modifiable areal unit problem (MAUP). The paper provides a systematic review of past literature on place-based community food environmental research using different analysis units or geospatial models as they pertain to the MAUP. We identify these key findings: (1) the ZIP code zone is not recommended as an appropriate analysis unit for modeling community food access, as it did not have significant correlations with health indicators; (2) using a circular buffer of less than 0.5 km around household locations is most likely to reveal health correlations, compared with network buffers or container-based measures; (3) to reveal health effects of the community food environment, it is recommended to focus in selected regions or partitions of a study area with similar socioeconomic statuses, such as the central city or low socioeconomic status areas; (4) for studies utilizing a single statistical unit or distance measure, it is suggested to discuss the existence of the MAUP, such as evaluating the sensitivity of the model to the change of the unit or the distance measure. By highlighting the MAUP, this paper has policy implications—given that geospatial modeling of food accessibility provides support for health policy intervention, using different metrics may lead to different interpretations of health disparities and could thus misinform policy decisions. Therefore, any assessment of community food environments that may potentially lead to a policy change should consider the effects of the MAUP. [ABSTRACT FROM AUTHOR]

The ongoing COVID-19 pandemic has taken a heavy toll on the physical and mental health of the public. Nevertheless, the presence of green and blue spaces has been shown to be able to encourage physical activities and alleviate the mental distress caused by COVID-19. However, just as the impact of COVID-19 varies by geographical region and area, the distribution of green and blue spaces is also different across different neighborhoods and areas. By using Hong Kong as the study area, we determine the local neighborhoods that suffer from both high COVID-19 infection risk as well as low green and blue space accessibility. The results show that some of the poorest neighborhoods in the territory such as Sham Shui Po, Kwun Tong and Wong Tai Sin are also among the most doubly disadvantaged in terms of COVID-19 infection risk as well as green and blue space accessibility. [ABSTRACT FROM AUTHOR]

Time allocation is closely related to life quality and is a potential indicator of urban space utilization and sociospatial differentiation. However, existing time allocation studies focus on how time is allocated to various activities but pay less attention to where individuals allocate their time. In the context of China's transformation, this study examines the differences in time allocation in different urban spaces between low- and non-low-income groups based on two methods, descriptive statistics and social area analysis. The results show that low-income participants' daily activities (especially work) are highly dependent on the central city area. However, they are at a disadvantage in accessing the central city area. Nevertheless, non-low-income individuals have diversified activity spaces and can better choose locations according to the purpose of activities and make fuller use of various types of urban areas. This study indicates that there are social differences in time allocation and urban space utilization among different income groups. The results obtained with regression models reveal that in addition to income, activity characteristics and built environment characteristics are significant factors affecting the differences. Social policies should support the equitable distribution of urban resources for different social groups, especially for vulnerable groups who live in affordable housing. [ABSTRACT FROM AUTHOR]

Human interaction with indoor objects constantly changes indoor space and its navigability. Spatial subdivision models that delineate navigable space become a crucial prerequisite for indoor navigation. However, existing spatial subdivision models do not fully capture the dynamic changes of the indoor context and have limitations in identifying precise navigable space, thereby reducing the accuracy and efficiency of indoor navigation. This study proposes a novel interactive semantic model (ISM) that consists of an interactive semantic base (ISB) and empirical rules to accurately determine the navigability of the reshaped functional spaces (F-Spaces) of indoor objects. First, two-level F-Spaces (fine-grained resource F-Space and coarse-grained structure F-Space) are defined to express multi-granularity interactive semantics for delineating heterogeneous F-Spaces. Second, empirical rules are established through an extensible multi-dimensional semantics classification framework to determine each F-Space's navigability. Lastly, a navigable F-Space generation scheme is designed by considering the adaptive navigability of the two-level F-Spaces. Simulated experiments show that the proposed model can generate precise and efficient dynamic navigable spaces. This study reduces the cognitive burden of human agents when facing indoor dynamic navigation, thereby improving the spatial experience of navigation. [ABSTRACT FROM AUTHOR]

Many types of spatial flows, including pedestrian flows and vehicle flows, are constrained by and distribute on spatial networks. In the literature, network‐constrained flows are usually modeled as a direct line in planar space using methods designed for flows in planar space. Further, in spatial statistical analysis of flow patterns, distance measures and the hypothesis of spatial randomness of flows also have a significant impact on the determination of flow patterns. In this study, we extend the global and local Ripley's K functions for planar flows to network space. Both the network and planar K‐functions for flows are applied to detect the patterns of taxi Origin‐Destination flow data on a road network at multiple scales. The effect of distance measures and simulation methods in the network and planar Ripley's K functions are examined. We found that the planar K function is more sensitive to the changes in scale and tends to detect more clustered flows compared with the network K function at the same scale. Distance measures and simulation methods have a more significant influence on the detection of patterns of network‐constrained flows than the selection of the network or planar Ripley's K functions. This study suggests that distance measures and hypotheses of spatial randomness have to be chosen carefully before applying flow pattern analytic methods to network‐constrained flows and interpreting the results of flow patterns. [ABSTRACT FROM AUTHOR]

The widespread use of personal geospatial data raises serious geoprivacy concerns for sharing these data, which may limit the reproducibility of research findings. One widely used method for securely sharing confidential geospatial information is applying geomasking techniques before sharing. Geomasking may reduce the usability of the data. Thus, researchers need to strike a balance between privacy protection and analytical accuracy. Although many geomasking methods have been proposed, there is no systematic evaluation of these methods or guidance on which method to use and how to apply it properly. To address this gap, we evaluate eight geomasking methods with simulated geospatial data with various spatial patterns and investigate their performance on privacy protection and analytical accuracy. We propose not only a set of preliminary guidelines for applying the proper geomasking methods when using different spatial analysis methods but also an evaluation framework for assessing geomasking methods for other spatial analysis methods. The findings will help researchers to properly apply geomasking for sensitive geospatial data and thus promote data sharing and interdisciplinary collaboration while protecting personal geoprivacy. [ABSTRACT FROM AUTHOR]

Many geographic studies examined the spatial relationships between store locations that promote urban growth through agglomeration effects. One of the key relationships is co‐location, the spatial proximity among different types of facilities within a specific distance. To analyze co‐location with statistical inference, several studies have employed the cross K function method. However, the ordinary cross K function method is likely to be unsuitable because it assumes a continuous and homogeneous two‐dimensional plane, while stores are usually located on a street network. Another drawback is that it does not directly specify how the distances between different types of stores influence their mutual attraction over space, referred to as the agglomeration zone in this article. To address these limitations, this article develops a new statistical method named the incremental network dual K function. The usefulness of the method is illustrated by an empirical analysis of store locations in a central area of Tokyo. [ABSTRACT FROM AUTHOR]

Copyright of Annals of the American Association of Geographers is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Geographer is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Spatial flow co-location patterns (FCLPs) are important for understanding the spatial dynamics and associations of movements. However, conventional point-based co-location pattern discovery methods ignore spatial movements between locations and thus may generate erroneous findings when applied to spatial flows. Despite recent advances, there is still a lack of methods for analyzing multivariate flows. To bridge the gap, this paper formulates a novel problem of FCLP discovery and presents an effective detection method based on frequent-pattern mining and spatial statistics. We first define a flow co-location index to quantify the co-location frequency of different features in flow neighborhoods, and then employ a bottom-up method to discover all frequent FCLPs. To further establish the statistical significance of the results, we develop a flow pattern reconstruction method to model the benchmark null hypothesis of independence conditioning on univariate flow characteristics (e.g. flow autocorrelation). Synthetic experiments with predefined FCLPs verify the advantages of our method in terms of correctness over available alternatives. A case study using individual home-work commuting flow data in the Chicago Metropolitan Area demonstrates that residence- or workplace-based co-location patterns tend to overestimate the co-location frequency of people with different occupations and could lead to inconsistent results. [ABSTRACT FROM AUTHOR]

In many central districts in cities across the world, different types of stores form clusters resulting from the benefits of spatial agglomeration. To precisely analyze co-location relationships in a micro-scale space, this study develops a new statistical method by addressing the limitations of the ordinary cross K function method. The objectives of this paper are, first, to formulate an exact statistical method for analyzing co-location along streets in a central district constrained by a street network; second, to implement this statistical method in computational procedures. Third, this method is extended to the analysis of repulsive-location, i.e. phenomena of stores locating repulsively among different types of stores. Fourth, the paper shows a graph-theoretic diagram illustrating the spatial structure of stores in a central district consisting of bilateral, unilateral co-location and repulsive-location. Last, the proposed method is applied to eight different types of stores in a trendy district in Tokyo. The results show that the method is useful for revealing the spatial structure consisting of co-location and repulsive-location in the central district. [ABSTRACT FROM AUTHOR]

This paper seeks to evaluate and calibrate data collected by low-cost particulate matter (PM) sensors in different environments and using different aggregated temporal units (i.e., 5-s, 1-min, 10-min, 30 min intervals). We first collected PM concentrations (i.e., PM1, PM2.5, and PM10) data in five different environments (i.e., indoor and outdoor of an office building, a train platform and lobby of a subway station, and a seaside location) in Hong Kong, using five AirBeam2 sensors as the low-cost sensors and a TSI DustTrak DRX Aerosol Monitor 8533 as the reference sensor. By comparing the collected PM concentrations, we found high linearity and correlation between the data reported by the AirBeam2 sensors in different environments. Furthermore, the results suggest that the accuracy and bias of the PM data reported by the AirBeam2 sensors are affected by rainy weather and environments with high humidity and a high level of hygroscopic salts (i.e., a seaside location). In addition, increasing the aggregation level of the temporal units (i.e., from 5-s to 30 min intervals) increases the correlation between the PM concentrations obtained by the AirBeam2 sensors, while it does not significantly improve the accuracy and bias of the data. Lastly, our results indicate that using a machine learning model (i.e., random forest) for the calibration of PM concentrations collected on sunny days generates better results than those obtained with multiple linear models. These findings have important implications for researchers when designing environmental exposure studies based on low-cost PM sensors. [ABSTRACT FROM AUTHOR]

This article provides a state-of-the-art summary of location privacy issues and geoprivacy-preserving methods in public health interventions and health research involving disaggregate geographic data about individuals. Synthetic data generation (from real data using machine learning) is discussed in detail as a promising privacy-preserving approach. To fully achieve their goals, privacy-preserving methods should form part of a wider comprehensive socio-technical framework for the appropriate disclosure, use and dissemination of data containing personal identifiable information. Select highlights are also presented from a related December 2021 AAG (American Association of Geographers) webinar that explored ethical and other issues surrounding the use of geospatial data to address public health issues during challenging crises, such as the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Copyright of Urban Studies (Sage Publications, Ltd.) is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The quality of urban settlement is an essential element of people's desire for a better life, which has been capitalized into housing prices of local cities. However, previous literature focuses overwhelmingly on the determinants of intracity housing prices. There is still a lack of research on the determinants of the spatial disparity in intercity housing prices, and there is even less research on the association between comprehensive urban settlement quality and housing prices. This paper uses principal component analysis to evaluate urban settlement quality for 294 cities in China and applies spatial econometric models to examine the impact of urban settlement quality on housing prices. The results show that urban settlement quality in China is composed of five principal components: social livelihood, service industry development, leisure-traffic, the ecological environment, and economic development. There is spatial correlation between urban settlement quality and housing prices in China, both of which show high values in eastern China and low values in western China. Spatial econometric modeling results indicate that social livelihood and the ecological environment have a significant positive impact on housing prices, while service industry development and the leisure-traffic factor have a significant impeding effect on housing prices. In addition, residential land prices and per capita real-estate development investment are significant factors affecting housing prices in China. [ABSTRACT FROM AUTHOR]

This study compares the space‐time patterns and characteristics of high‐risk areas of COVID‐19 transmission in Hong Kong between January 23 and April 14 (the first and second waves) and between July 6 and August 29 (the third wave). Using space‐time scan statistics and the contact tracing data of individual confirmed cases, we detect the clusters of residences of, and places visited by, both imported and local cases. We also identify the built‐environment and demographic characteristics of the high‐risk areas during different waves of COVID‐19. We find considerable differences in the space‐time patterns and characteristics of high‐risk residential areas between waves. However, venues and buildings visited by the confirmed cases in different waves have similar characteristics. The results can inform policymakers to target mitigation measures in high‐risk areas and at vulnerable groups, and provide guidance to the public to avoid visiting and conducting activities at high‐risk places. [ABSTRACT FROM AUTHOR]

Background: Urban parks are critical environmental resources in which adolescents engage in physical activity (PA). Evidence on the associations between park environmental characteristics and park-based PA in adolescents is mixed, particularly for high-density cities. Evidence is also lacking concerning the moderating role of neighbourhood socioeconomic status on the park-PA relationships. The current study aimed to examine the associations between park environmental characteristics and moderate-to-vigorous physical activity (MVPA) in parks among adolescents in Hong Kong and the moderating effect of neighbourhood income on these associations. Methods: A cross-sectional study involving direct observations of adolescents was conducted in 32 randomly selected urban parks in Hong Kong. Park environmental characteristics were measured using the Community Park Audit Tool. Park-based MVPA among adolescents was measured using the System for Observation Play and Recreation in Communities. Neighbourhood income was extracted from the 2011 Hong Kong Population Census data on median household income. Results: There was a significant positive association between the quality of amenities and park-based MVPA (metabolic equivalents per observation) in adolescents. However, the associations between the diversity of active facilities, greenness and adolescents' park-based MVPA were not significant. Neighbourhood income moderated the association between adolescents' park-based MVPA and park safety, where the relationship between park safety and park-based MVPA was significantly positive in low-income neighbourhoods but not significant in high-income neighbourhoods. An income-by-environment interaction was also observed concerning park aesthetics, with a negative relationship between park aesthetics and park-based MVPA in high-income neighbourhoods but not in low-income neighbourhoods. Conclusion: Our findings provide evidence regarding how park environment and neighbourhood income impact adolescents' park-based MVPA in Hong Kong. These findings can inform urban planning and policymakers who seek to improve urban park development in high-density cities. [ABSTRACT FROM AUTHOR]

This article examines the effects of two widely used geomasking methods (aggregation and the bimodal Gaussian method) on errors in car‐ and transit‐based travel times from people's homes to health facilities using Cook County in Illinois as a case study area. It addresses two research questions: (Q1) How do the effects of geomasking on travel time errors differ between transportation modes? (Q2) How do errors in car‐ and transit‐based travel times differ between urban and suburban areas? The results indicate that geomasking introduces considerable errors in travel times. Specifically, errors in transit‐based travel times are significantly higher than those in car‐based travel times. Moreover, when large radii are used for geomasking, errors in car‐based travel times in urban areas are significantly higher than those in suburban areas. On the contrary, transit‐based travel time errors in urban areas are significantly lower than those in suburban areas. Because transportation modes and urban area types play essential roles in travel time errors caused by geomasking, researchers need to mitigate these errors when using geomasked locations for their analysis (e.g., evaluating the spatial accessibility of certain facilities, such as hospitals or healthy food outlets). [ABSTRACT FROM AUTHOR]