Using six waves of the Chinese General Social Survey (2010–2021) and cross-classified random effects modeling, this study investigates regional and cohort variations in intergenerational mobility and transmission processes in China. Results show that regional patterns in the levels of intergenerational mobility and in the effects of determinants of status attainment are relatively stable across birth cohorts from the 1940s to the 1990s. First-tier cities and eastern regions consistently exhibit higher absolute mobility and greater influence of socioeconomic achieved factors, while the influence of socioeconomic ascribed factors and institutional factors such as party membership and hukou is weaker. Fixed-effects panel analyses demonstrate that regional economic development is associated with greater absolute mobility and weaker institutional effects, while coinciding with strengthened roles of socioeconomic ascribed and achieved factors. Educational inequality aligns with heavier reliance on institutional factors and correlates positively with relative and absolute mobility. Higher public education expenditure is linked to greater absolute mobility and diminished ascriptive influences. The findings highlight that China’s intergenerational mobility is shaped by intertwined economic, institutional, and policy dynamics, with enduring regional disparities despite overall progress towards a merit-based system.

Despite considerable focus on clustering as a dimension of segregation and the explosion of big location data, the extant literature has not explicitly examined residential racial segregation and the clustering of racially segregated space as an influence on mobility. Drawing on urban sociological theories, we test criteria contributing to individuals’ selection of key activity neighborhoods. Using a range of spatial data sources, we compare White and Black individuals’ choice of frequently visited neighborhoods in Chicago, stratified by whether residing in a contiguous segregated cluster (CSC). Discrete choice models show evidence for the impact of clustered residential segregation in individual decision-making. Net of distance, all groups are drawn to White CSC neighborhoods. White residents exhibit a pattern of geographic isolation, gravitating toward White CSC tracts and away from Black spaces, CSC and non-CSC alike. Black residents of Black CSC neighborhoods are more likely to have activity locations in White CSC neighborhoods than their own residential CSC, largely because of the relative institutional, amenity, and crime-related advantages of these areas. Results are robust to alternative specifications of choice sets and institutional deficits. Implications for understanding the social context of routine location choice and designing desegregation policies through behavioral “nudges” are discussed.

OBJECTIVES:Momentary feelings of safety carry significant implications for well-being in later life. Yet the role of social and built environments beyond residential neighborhoods remains underexplored. We investigated how dynamic social and physical features and spatial knowledge of activity spaces affect older adults' in situ feelings of safety. METHODS:Using unique data from the Chicago Health and Activity in Real-Time (CHART) study, we analyzed 5,833 geolocated ecological momentary assessments (EMAs) from 93 older adults. We linked EMA locations to Google Street View Images to measure streetscape features and Advan mobility flows to capture spatial knowledge. We estimated race-stratified multilevel models with EMAs nested within respondents and used a within-between decomposition to distinguish momentary exposure from between-person differences in average exposure. RESULTS:Momentary safety varied substantially across contexts, with distinct racial patterns. Being outdoors reduced safety for both race groups. White respondents reported lower safety in areas predominantly Black and of low socioeconomic status (SES) but greater safety in racially mixed, higher-SES neighborhoods. In contrast, Black respondents felt less safe in racially mixed and affluent White neighborhoods, reflecting varied experiences of being socially out of place. Physical disorder and built-environment cues predicted safety only among Whites, indicating that the meaning of disorder is socially conditioned rather than universal. Across both groups, spatial knowledge consistently increased perceived safety. DISCUSSION:Findings underscore the situational and racialized nature of safety in later life. While White older adults' safety perceptions respond to visible streetscape cues, Black older adults' perceptions reflect broader structural and contextual neighborhood characteristics.

ABSTRACT Objectives Integrating recent advances in computer vision techniques and mobile phone mobility data, this study extends journey-to-crime research by explicitly examining how burglars’ neighborhood context and spatial knowledge shape their interpretation of physical disorder during spatial target selection. Methods We measure observed and perceived physical disorder from a total of 107,858 street view images using computer vision algorithms. Geo-referenced mobile phone flows between 1,642 census units are used to approximate offenders’ potential spatial knowledge about target neighborhoods. Discrete choice models are estimated separately for burglars from disadvantaged and non-disadvantaged neighborhoods (N = 1,972). Results While burglars residing in non-disadvantaged neighborhoods are not sensitive to physical disorder in non-disadvantaged target neighborhoods, they strongly avoid disadvantaged neighborhoods with disorder. Conversely, residents of neighborhoods with concentrated disadvantage act swiftly on street disorder in better-off neighborhoods but not in disadvantaged neighborhoods. These tendencies to react to physical disorder on the street are further amplified by burglars’ potential spatial familiarity with the target environment. Conclusions We highlight the importance of larger neighborhood structural characteristics and their interactions with spatial knowledge and environmental conditions such as visual signs of disorder, in burglary decision-making. Physical disorder is not uniformly indicative of decay across neighborhoods and offenders. Moreover, spatial knowledge is most effective in triggering or deterring actions in places that are categorically different from offenders’ residential spaces. We discuss the strengths and challenges of our multi-source computational approach for criminology research.

Background Loneliness poses a major public health concern for older adults, with significant implications for well-being and mortality. While prior studies have examined individual and social risk factors, the role of the built environment in shaping momentary experiences of loneliness remains underexplored. This study investigates how built environment features beyond the residential neighborhoods affect older adults’ momentary feelings of loneliness in activity spaces. Methods Drawing on data from the Chicago Health and Activity in Real-Time (CHART) study, we analyze more than 7000 geolocated ecological momentary assessments (EMA) from 111 older adults. We extract streetscape features (e.g., green space, human presence, and vehicles) from Google Street View Images (SVI) and points-of-interest (POI) indicators capturing nearby destinations such as organizations, transit amenities, and hospitals. Using multilevel models, we distinguish within-person fluctuations from between-person differences in loneliness across hyperlocal ecological contexts. Results Our findings show that activity-space environments are significantly associated with momentary loneliness. Environmental and institutional stressors are particularly salient: exposure to more hospital-dense environments than usual, as well as greater routine exposure to transit amenities and vehicular traffic, is positively linked to momentary loneliness. By contrast, evidence for social affordance and restorative environments, such as green space, is limited. Individual and situational factors, including being married, being alone, and time of day, also predict loneliness, alongside strong temporal persistence across moments. Conclusions Our results highlight the situational and contextual nature of loneliness and suggest that age-friendly urban planning should attend to everyday activity spaces in addition to residential neighborhoods.

Socio-economic data with fine-grained spatial resolution forms the basis of socio-spatial analysis and policymaking. In response to the limited availability of such data in China, this study provides an open-access, community-level dataset on education percentile rank — a more accurate indicator of social status than years of education. Our dataset comprises 122,126 communities, covering 97.9% of prefecture-level administrative units and 81.8% of county-level administrative units. The data is estimated using an XGBoost machine learning model based on the relationship between mean education percentile rank and the characteristics of the built environment, including functions and facilities, street scene elements, vitality, human perception, physical disorder, and topography at the community level. Multi-source data, including the Chinese General Social Survey, points of interest, road networks, night-time lighting, and street view images processed using computer vision techniques such as semantic segmentation, object detection, and image regression, are used for model training and inference. Our final education predictions are highly accurate at prefecture, county, and community levels. This dataset enables fine-grained socio-spatial analyses across disciplines.

Human perception is often considered a comprehensive evaluation of environmental quality. It is an important indicator of neighbourhood socioeconomic status and has a significant impact on various social outcomes. In response to the absence of locally trained models based on Chinese street view images and local annotators, we present two datasets. Dataset I, the perceived wealth and physical disorder scores annotation dataset, consists of 40,000 Chinese street view images that are annotated by local urban planners using the image comparison approach. Researchers can use Dataset I directly or further augment it to train their own artificial intelligence perception models in China. We use Dataset I to train image regression models, which are then employed to infer two perception scores for 36,262,700 street view images throughout urban China between 2013 and 2022. The resulting Dataset II, the perceived wealth and physical disorder scores prediction dataset, comprises three analytical units including image shooting points, 500m×500m grid cells, and 76,434 community administrative areas. Dataset II supports a variety of wide-coverage, fine-grained socio-spatial research projects in China, including studies on inequality, segregation, and gentrification. It can also serve as a critical input for examining other important socio-spatial phenomena, such as crime and physical activity patterns.

As a result of variations in offender activity patterns in crime hotspots and non-hotspots, the spatial strategies of policing (such as the quantity and spatial coverage) should differ. Existing literature has not examined the spatially heterogeneous effect of police stop strategies on crime. This study focuses on the police stops-crime nexus in China, using the case of a major city’s central district. We introduce the spatial dimension of police stops—spatial coverage—in addition to the commonly considered quantity of police stops. Our results indicate that the spatial coverage of police stops surpasses the number of police stops as the most important crime predictor. We further find spatial heterogeneity in the police stop-crime nexus. A larger amount of police stops and spatially focused stops are more effective in crime hotspots. In crime non-hotspots, higher spatial coverage of police is more important in deterring crime than a larger number of police stops.

Objectives Although the social disorganization tradition emphasizes the role of neighborhood context in shaping delinquent behaviors and neighborhood crime, researchers have rarely considered the influence of neighborhood context on criminals’ decision of where to offend. This study explicitly examines how concentrated disadvantage in both the origin and destination neighborhoods structures burglars’ preference for street physical disorder and spatial familiarity.Methods We measure observed and perceived physical disorder from 107,858 street view images using computer vision algorithms. Geo-referenced mobile phone flows between 1,642 census units are used to approximate offenders’ potential spatial knowledge about target neighborhoods. Discrete choice models are estimated separately for burglars from disadvantaged and non-disadvantaged neighborhoods (N=1,972).Results While burglars residing in non-disadvantaged neighborhoods are not sensitive to physical disorder in non-disadvantaged target neighborhoods, they strongly avoid disadvantaged neighborhoods with disorder. Conversely, residents of neighborhoods with concentrated disadvantage swiftly act upon street disorder in better-off neighborhoods but not in disadvantaged neighborhoods. These tendencies to react to the presence of physical disorder on the street are also contingent on burglars’ potential familiarity with the target environment.Conclusions We highlight the importance of larger neighborhood structural characteristics and their interactions with spatial knowledge and environmental conditions such as visual signs of disorder, in criminal decision making. Physical disorder is not uniformly indicative of decay across neighborhoods and offenders. This divergent decision-making may also partially explain spatial heterogeneity of crime. Moreover, spatial knowledge is most effective in triggering or deterring actions in places that are categorically different from offenders’ residential spaces.

Home is a locus of everyday activity among a growing population of Americans who are "aging in place," and for whom leaving the home is generally thought to benefit quality of life and well-being. Sociological and criminological theory has often assumed that higher levels of local crime constrain individuals' activities to the residential environment, although few studies have empirically tested this assumption. We use longitudinal smartphone-based GPS data and ecological momentary assessments from 409 older adults in the Chicago Health and Activity Space in Real-Time study, linked with administrative crime data, to test this relationship through a series of multilevel linear regression models. Our findings suggest that older adults living in higher crime areas spend less time at home, on average, compared to older adults who live in lower crime areas. This association is especially evident among older adults who experience higher levels of unsafety at home. We discuss the implication that neighborhood characteristics can permeate the boundaries of the home, adversely affecting an already vulnerable population in ways that may exacerbate inequality in community engagement, collective efficacy, and health. Our findings prompt a more nuanced understanding of what leaving the home represents among the aging population.

A longstanding urban sociological literature emphasizes the geographic isolation of city dwellers in residence and everyday routines, expecting exposures to neighborhood racial and socio-economic structure driven principally by city-wide segregation and the role of proximity and homophily in mobility. The compelled mobility approach emphasizes the uneven distribution of organizational and institutional resources across urban space, expecting residents of poor Black-segregated neighborhoods to exhibit non-trivial levels of everyday exposure to White, non-poor areas for resource seeking. We use two sets of location data in the hypersegregated Chicago metro to examine these two approaches: Global Positioning System (GPS) location tracking on a sample of older adults from the Chicago Health and Activity Space in Real-Time (CHART) study and travel diaries on a sample of younger adults by the Chicago Metropolitan Agency for Planning (CMAP). We introduce a novel and flexible individual-level method for assessing activity space exposures that accounts for the spatially proximate environment around home. Analyses reveal that activity space contexts mimic the racial/ethnic and socio-economic landscape of respondents' broad residential environment. However, after residential-based adjustment, Black younger (CMAP) adults from poor Black neighborhoods are disproportionately exposed to Whiter, less Black but less non-poor neighborhoods. Older (CHART) adult activity spaces align more closely with their residential areas; however, activity spaces of poor-Black-neighborhood-residing CHART Blacks are systematically poorer and, less consistently, more Black and less White after local area adjustment. Implications for understanding contextual exposures on well-being and the potential for age or cohort differences in isolation are discussed.

Neighborhood Context Shapes Effects of Physical Disorder and Spatial Knowledge on Burglars’ Location Choice: A Multi-Source Approach ABSTRACT Objectives Although the social disorganization tradition emphasizes the role of neighborhood context in shaping delinquent behaviors and neighborhood crime, researchers have rarely considered the influence of neighborhood context on criminals’ decision of where to offend. This study explicitly examines how concentrated disadvantage in both the origin and destination neighborhoods structures burglars’ preference for street physical disorder and spatial familiarity. Methods We measure observed and perceived physical disorder from 107,858 street view images using computer vision algorithms. Geo-referenced mobile phone flows between 1,642 census units are used to approximate offenders’ potential spatial knowledge about target neighborhoods. Discrete choice models are estimated separately for burglars from disadvantaged and non-disadvantaged neighborhoods (N=1,972). Results While burglars residing in non-disadvantaged neighborhoods are not sensitive to physical disorder in non-disadvantaged target neighborhoods, they strongly avoid disadvantaged neighborhoods with disorder. Conversely, residents of neighborhoods with concentrated disadvantage swiftly act upon street disorder in better-off neighborhoods but not in disadvantaged neighborhoods. These tendencies to react to the presence of physical disorder on the street are also contingent on burglars’ potential familiarity with the target environment. Conclusions We highlight the importance of larger neighborhood structural characteristics and their interactions with spatial knowledge and environmental conditions such as visual signs of disorder, in criminal decision making. Physical disorder is not uniformly indicative of decay across neighborhoods and offenders. Moreover, spatial knowledge is most effective in triggering/deterring actions in places categorically different from offenders’ residential spaces. We discuss the strengths and challenges of our multi-source computational approach for criminology research.

Using novel Internet search data from Baidu Index, this study examines for the first time the nationwide distribution of city-level intensity of online suicidal ideation in China and the underlying social determinants and processes. We find that the intensity of suicidal ideation shows moderate spatial clustering, decreasing from east to west nationally and from developed to less developed areas within each province. Overall, socioeconomic inequality, social fragmentation as represented by single-generation households and religiosity, and the proportion of older adults are positively associated with suicidal ideation. Social deprivation, divorce rate, and male-to-female sex ratio have significant negative effects on suicidal ideation, while marriage rate has insignificant effects. Further analyses based on geographically weighted regression suggest that the direction, magnitude, and statistical significance of the set of risk factors relevant to suicidal ideation vary by contexts and that city-specific interventions for suicide prevention are needed.

Activity space research explores the behavioral impact of the spaces people move through in daily life. This research has focused on urban settings, devoting little attention to non-urban settings. We examined the validity of the activity space method, comparing feasibility and data quality in urban and non-urban contexts. Overall, we found that the method is easily implemented in both settings. We also found location data quality was comparable across residential and activity space settings. The major differences in GPS (Global Positioning System) density and accuracy came from the operating system (iOS versus Android) of the device used. The GPS-derived locations showed high agreement with participants' self-reported locations. We further validated GPS data by comparing at-home time allocation with the American Time Use Survey. This study suggests that it is possible to collect daily activity space data in non-urban settings that are of comparable quality to data from urban settings.

Objectives To examine the causal impact of small businesses on street theft and the underlying mechanisms. Methods The “Cleanup Holes in the Wall” campaign in Beijing, China, provides a rare opportunity for a natural experiment. Drawing on street view images processed by deep learning algorithms and other big data sources such as court judgments and location-based service (LBS) population, we use difference-in-difference (DID) models to investigate how the disappearance of small businesses leads to changes in the occurrence of theft. We further examine the mechanisms by introducing mediators, including ambient population and social activity. Results The treatment units that experienced a mass loss of small businesses showed a significant reduction in street theft compared to the control units that were less affected by the cleanup campaign. Ambient population and social activity played a mediating role in promoting and deterring crime, respectively, with the former dominating. The results remain robust after including covariates in the models, balancing covariates using the propensity score matching method, and adopting alternative thresholds to classify the treatment group. Conclusions There are two competing yet coexisting mechanisms through which small businesses influence street theft. On the one hand, commercial premises provide large numbers of criminal opportunities for potential offenders; on the other hand, they are central to local social control and order. While small businesses exercise a certain amount of natural surveillance power, as a whole, they function primarily as crime generators. Implications for implementing targeted policies tailored to the nature of small businesses are discussed.

This study examines the temporal changes in income segregation within the ambient population around the clock using mobile phone big data. It employs ordinal entropy, a metric suited for measuring segregation among ordered groups, to quantify the level of segregation among eight income groups within micro-geographic units throughout the 24-h period on a weekday and a weekend day in the urban core of Guangzhou, China. The study further decomposes daily segregation by location and time profile. We identify urban functions and neighborhood contexts relevant for income segregation and explore their temporal variation. Using group-based trajectory analysis, we classify daily segregation trends among 400 m urban grids into seven distinct trajectories for both weekday and weekend. Our findings confirm that segregation fluctuates constantly. The role of local urban functions, particularly retail, accommodation, and offices, and neighborhood context, such as the number of residents and the share of non-local migrants, exhibits a significant temporal rhythm. The seemingly convoluted 24-h segregation time series among urban grids follow just a few distinct trajectories with clear geographical patterns. There is limited variability at individual grids both over the course of a day and across days. Shifts across different trajectory types between weekday and weekend are rare. The dynamic daily segregation in the ambient population per se may be an enduring characteristic of neighborhoods and a real-time channel for neighborhood contextual influences, potentially fueling long-term residential segregation and neighborhood change.

This article constructed two spatial indices to better understand the interactions between social sustainability (an important but poorly defined concept) and exposure to climatic and environmental risks. The indices, and the Choropleth maps used to represent them, can be combined and operationalized across different country contexts to yield insights into how climate change and social vulnerabilities intersect and can be jointly addressed. The two indices were here applied to Vietnam, a country particularly exposed to climate change. While Vietnam is well-known for its vulnerability to changing temperatures and rising sea levels, there was huge variation within and between regions for these two risks. The analysis also found enormous spatial variation within the risks from precipitation, drought, deforestation, and air pollution. Social inclusion generally outperformed resilience and social cohesion, as well as empowerment in Vietnam. Our findings were robust for choices of indicators, weights, and aggregation specifications.

Recent studies focus on how to accurately measure ambient population and its effects on crimes. However, limited attention has been paid to the internal heterogeneity of ambient population composition and its temporal variation. To fill these gaps, controlling for indicators of ambient population, social disorganization, crime generators and attractors, we model the effects of the share of low income, middle income, upper-middle income and high income of ambient population at the community level in the whole and specific time periods on weekdays and weekends. Results suggest that while all income groups of the ambient population contribute to theft at the community level, their contributions are not uniform. At different periods of both weekdays and weekends, there is no single income group that consistently predicts theft. Furthermore, the magnitude of the impact of each income group fluctuates with time. Our results highlight the heterogeneous segments of the ambient population, coupled with the dynamic human mobility patterns, structure the convergence of the crime triangle – motivated offenders, potential targets, and guardianship – in nuanced yet crucial ways.

To advance the interpretability of machine learning for long-term crime prediction in China, we compared the performance of multiple machine learning algorithms in predicting the spatial pattern of theft in Beijing. Gradient boosting decision tree emerged as the algorithm with best predictive accuracy. After identifying the importance of criminogenic features, we extended the interpreter SHAP to reveal nonlinear and spatially heterogeneous associations between environmental features and theft and we summarized six relation types of such associations at the global scale. At the local scale, we clustered six area types according to the contribution of environmental attributes to theft prediction in each grid. Policy makers should adopt place-based crime prevention measures based on the specific type of each grid belongs to.

The segregation–crime relationship is a classic topic in sociology and crime geography, yet existing literature mainly focuses on the impact of racial segregation at the global scale. Little is known about the impact of local segregation of other socioeconomic characteristics such as education level, an important segregation factor for racially homogenous countries like China. Also unknown is their impact beyond the residential domain. Using the Baidu Map Location-Based Service population data set and court records in 863 local geographic units of the central urban area of Beijing during 2018 and 2019, this study uncovers the spatial pattern of segregation between people with and without a bachelor's degree measured in the residential space and activity space and further investigates the influence of these two types of educational segregation and their interaction effects with social context on theft and violent crime. Results show less segregation in the activity space than in the residential space. Both types of segregation, however, significantly increase the risk of theft and violence, with activity space–based segregation more consequential. Moreover, the positive segregation–crime link is moderated by the local social context measured by the educational composition among residents and the ambient population. Compared with residential segregation, activity space–based segregation is more detrimental for places dominated by the less educated. Our results highlight the elevated influence of segregation on safety beyond the residential space, especially for areas clustered with the less educated ambient population.