Marijuana is the most widely used illicit drug in the United States. The recent legalization of marijuana has shifted public opinion, which might affect how people report drug-related activities. Whereas some studies have investigated the issue at the city or larger scales, few have focused on a microlevel within a city. This study examined the spatiotemporal distribution of drug-related calls at the street segment level in Cincinnati, Ohio, from 2013 to 2019, to test if the legalization of medical marijuana on 8 September 2016 had an impact. Results indicated that the seasonal drop in calls was steady after the legalization, except for an immediate increase from fall 2015 to fall 2016, which is probably due to a lag effect from the peak volumes of calls in spring and summer of 2016. The spatial distribution of the calls became more concentrated. Using a combination of zero-inflated negative binomial regression model and interrupted time-series analysis, this study confirmed that the effects of medical marijuana legalization on drug-related calls were statistically significant, with the spatial lag, the socioeconomic variables, crime generators and attractors, and the season dummy variable controlled. This research enhances our understanding of the relationship between medical marijuana legalization and drug-related calls at the street segment level and provides a method that can be applied in other areas. Findings on the increasing concentration of drug-related calls have important policy implications. The police department should consider developing more targeted intervention strategies that focus on areas with a high concentration of such calls.

Aiming at information fuzziness, uncertain decision-makers’ preferences and the lack of social network trust relationships in complex decisions, existing picture fuzzy information-based group decision-making methods have defects including a complex consistency check process, lack of objective weight allocation basis and difficulty in quantifying decision result reliability. This paper proposes a novel picture fuzzy information group decision-making method integrating the BrowseRank algorithm and judgment matrix consistency theory. Firstly, a breakthrough is made in judgment matrix consistency theory: transformation functions are used to convert picture fuzzy preference relations into fuzzy complementary judgment matrices to simplify data and reduce computational complexity. A derived matrix and an additive consistency determination theorem are proposed, solving cumbersome traditional verification processes. Secondly, innovations are made in social networks and weight allocation: a picture fuzzy trust propagation operator complements missing trust relationships; the BrowseRank algorithm is introduced to calculate decision-makers’ objective weights by integrating multiple parameters, breaking the limitation of the ignoring social attributes. An aggregation strategy combining the weighted geometric average operator and Hadamard product ensures the consistency of the aggregated matrix, filling relevant research gaps. Thirdly, a picture fuzzy set similarity measure defines decision-maker reliability, realizing a quantitative evaluation of decision result reliability and improving the evaluation system. Finally, the method is applied to new energy vehicle battery selection; its advantages in consistency check efficiency, weight rationality and result reliability are verified via simulation and comparative analysis.

Many studies have examined the relationship between street crime and public transit nodes, such as bus stops, subway stations, railway stations, and shared bike stations, but few have touched on streetcar stations, especially at the street segment level. Like other types of transit nodes, streetcar stations may function as crime generators, as suggested by crime opportunity theories. This study examines the potential impact of streetcar stations on street robberies in Cincinnati, OH. Unlike most studies that either explore the association between transit and crime or compare the associations before and after transit operation, this study compares longitudinal associations of three periods of pre-construction, construction, and post-construction of the streetcar stations. We use GIS techniques, negative binomial regression, and difference-in-differences (DID) models for the comparison. Results reveal that street robbery decreased from pre-construction to post-construction of the streetcar in Cincinnati but became increasingly concentrated on a few street segments. No association exists between the location of future streetcar stations and street robbery prior to construction; however, the association emerges during construction and becomes more significant post-construction, coupled with community conditions and Point-of-Interests (POIs). Therefore, what generated crime is streetcar stations instead of the locations of the streetcar stations. Further, the effects of streetcar stations are not as strong as those of the bus stops. These findings contribute to the literature on street crime and public transit. The DID model further underscores the possible causal effect of the streetcar stations on crime. Moreover, it also informs the local police department that additional focus is needed for streetcar stations, bus stops and their surrounding social and built environments.

Crime is not randomly distributed but tends to occur in specific spatial clusters. The literature has published many theories to explain its underlying causes. In recent years, scholars have increasingly leveraged social media big data to enrich our understanding of crime. Among these efforts is the Broken Emotion Conjecture, which offers a novel perspective on the connection between crime and emotion. However, how this connection varies among crime types and across the geographic space remains unclear. In this study, we investigate the spatial variations of the Broken Emotion Conjecture by analyzing emotion of residents and visitors, and their associations with assaults, burglaries, robberies, and thefts in Cincinnati, OH. Through spatial statistical analyses, we find that emotional states of residents and visitors have distinct effects on crime. Specifically, after controlling for key socioeconomic and land-use factors, we observed that collective negative emotion among residents is associated with a higher likelihood of burglaries; while collective negative emotion among visitors correlated with increased risk of assault, burglary, and robbery. Notably, we found no statistically significant impact of either residents’ or visitors' negative emotion on thefts. These findings align with established criminological and psychological theories, but provide a more nuanced interpretation of the connection between emotion and crime. Our study contributes to the growing body of research on the crime-emotion relationship, supports the development of an ambient population based emotion research within criminology, and provides practical policy implications.

Trips of the general population and criminals share many commonalities but also have obvious differences. Few studies have paid attention to such differences. The journey to crime has not been systematically compared to people's routine travel. To fill these gaps, this study utilized theft cases and cell phone mobility data to compare the differences between the journey-to-crime distance of thieves and home-originated trip distance of general population at the community level in one of the largest cities in China. The difference was further explained from the perspectives of social environment, built environment and transportation accessibility in both the home community and target community. Results show that thieves generally travel shorter than regular residents and the difference between the two distances gradually decreases from the inner city to the suburbs. The proximity from the home community to the city center, and shops, wholesale markets and non-local residents in the target community are positively associated with the difference, while old houses in the target community are negatively linked to the difference. These findings are important additions to the literature on journey-to-crime and human mobility. They also offer scientific insight for crime prevention, urban planning, and community police policy.

In recent years, the surge of delivery riders for the Internet platform economy has drawn widespread attention and sparked debate regarding their roles in urban governance. While some reported their traffic violations and disorderly conduct, 93 % of news reports observed their beneficial acts in urban governance. Whether the delivery riders can act as informal guardians who may potentially prevent crime has yet to be explored quantitatively in existing literature. To fill the gap, using mobile phone big data, this study identified and quantified the spatial distribution of delivery riders in ZG city. Then, this study employed a zero-inflated negative binomial model to analyze the relationship between their spatial distribution and street crime. Results showed that nearly 15,000 delivery riders of ZG city were identified from a large sample of mobile phone trajectory data, and they are mainly concentrated in economically active areas with busy commerce and service activities and dense populations. The number of riders' visits exhibits a significant negative association with street crime. This demonstrates that delivery riders can indeed act as informal guardians, which has not been previously reported in existing literature. This study enriches the theory of informal guardianship in the Internet platform economy era and highlights the social value of delivery riders in urban governance, offering practical insights for urban safety planning.

There exists abundant literature on vehicle theft, but only a few studies focused on bicycle theft and motorcycle theft. This study aims to reveal and explain differences in spatial distributions of bicycle theft and motorcycle theft in ZG city, China. The key findings are as follows: (1) There are spatial disparities in the hotspots of bicycle theft and motorcycle theft. Bicycle theft hotspots predominantly cluster in the urban core of ZG city, while motorcycle theft hotspots are primarily concentrated in the suburban regions. (2) At the community level, car parks, Internet cafes, and subway stations have a significant positive impact on bicycle theft, while bus stops and shops have a significant positive impact on motorcycle theft. The residential area has significant positive impacts on both bicycle and motorcycle thefts. (3) The proportion of the low-educated has a significant deterrent effect on bicycle theft but a positive impact on motorcycle theft, while the proportion of low-income residents significantly increases motorcycle theft. The proportion of migrant population and residential land area within communities have a significant positive impact on both bicycle theft and motorcycle theft. (4) Surveillance cameras have a significant positive impact on motorcycle theft, but ambient population density has a significant deterring effect on motorcycle thefts. Neither of these two guardianship variables have significant impacts on bicycle thefts. The main theoretical contribution of this study is that it provided a comprehensive assessment on the contrasting spatial distributions between bicycle thefts and motorcycle thefts and on the contrasting contributing factors for the two thefts. These findings provide a scientific basis for effective crime prevention and urban governance. A uniform strategy would not be able to prevent and reduce both bicycle thefts and motorcycle thefts. Effective strategy should target the high concentration areas and intervene the specific contributing factors for each of the two thefts.

In recent years, there has been a sharp increase in the number of online fraud cases. However, research on crime geography has paid little attention to online crimes, especially to the influencing factors behind their spatial distributions. Online fraud is closely related to people’s daily internet use. The existing literature has explored the impact of internet use on online crimes based on small samples of individual interviews. There is a lack of large-scale studies from a community perspective. This study applies the routine activity theory to online activities to test the relationship between online fraud alert data and the usage durations of different types of mobile phone users’ applications (apps) for communities in ZG City. It builds negative binomial regression models for analyzing the impact of the usage of different types of apps on the spatial distribution of online fraud. The results reveal that the online fraud crime rate and the online time spent on a financial management app share the most similar spatial distribution. While financial management, online education, transportation, and search engine app usages have a significant positive association with online fraud, the use of a financial management app has the greatest impact. Additionally, time spent on social media, online shopping and entertainment, and mobile reading apps have a significant negative association with online fraud. As not all online activities lead to cybercrime, crime prevention efforts should target specific types of apps, such as financial management, online education, transportation, and search engines.

Governments around the world implemented social distancing measures and lockdowns to limit people's movement and stem the spread of the global COVID-19 pandemic. These restrictions have changed the ambient population and altered its racial composition. By analyzing trips between census block groups using data from SafeGraph, we calculate the ambient population and infer its racial makeup during pre-lockdown, lockdown, early-post lockdown, and late-post lockdown periods in Cincinnati, Ohio. We examine the relationship between the ambient population-based racial heterogeneity (H) index and assault, robbery, and theft across the four periods. Our findings indicate that the lockdown affected mobility differently across racial groups. Additionally, we observe a stable, statistically significant influence of the ambient population-based H index on street crimes, in contrast to traditional census-based and spatial lagged measurements. This study demonstrates the effectiveness of the ambient population-based H index in explaining street crimes, particularly when people's routine interactions are significantly altered. It also contributes to theories of social organization, crime mobility, and routine activities.

Street segments have witnessed growing scholarly attention for their pivotal role in determining urban crime patterns. However, existing studies have not thoroughly considered the effects of refined street types on crime, let alone their interactive effects with business facilities on crime. This research aims to fill these gaps, by examining the relationship between theft, street type and Facilities in Cincinnati. Results show that alleys and pedestrians are less susceptible to theft than major and minor arterial streets. Interactive terms between street type and Facilities enhance the model's performance and reveal that the concentration of thefts is positively related to the association between Facilities and local streets or major arterial streets. Our findings offer not only add to the existing literature, but also offer insight on urban safety and planning practice.

Many scholars have established that facilities represented by Points-of-Interests (POIs) may function as crime generators and attractors, influencing criminal activities. While existing measurements of POIs primarily rely on quantitative counts, this count-based approach overlooks the spatial arrangement of POIs within an area, which can also contribute to crime. This paper introduces two methods to capture the spatial arrangement characteristics of POIs. One is called the normalized Shannon Voronoi Diagram-based Entropy (n_SVDE). A Voronoi diagram is constructed based on the spatial distributions of POIs in an area, resulting in polygons, each corresponding one POI. The area proportions of these polygons are then used to calculate Shannon Entropy. A low entropy value indicates a clustering pattern, while a high value reflects a dispersed distribution. The other is the average nearest neighbor distance ratio (ANN_ratio). It is a ratio of the average of the nearest distances of POIs in an area over the expected average. The effectiveness of these two methods is tested by using negative binominal models to explain street robberies in Cincinnati. Our findings show that the n_SVDE significantly explains street robbery, while the ANN_ratio shows no statistical significance. Specifically, a less clustered spatial distribution of POIs is positively associated with an increased likelihood of crime events, while a highly clustered distribution corresponds to a lower likelihood of crime. This study represents one of the pioneering implementations in explicitly examining the spatial configuration of POIs, contributing new insights into environmental criminology and providing valuable empirical evidence for enhancing place management and optimizing police patrols.

Perceived safety of the built environment-a cognitive assessment different from emotional fear of crime-might affect the number of potential crime victims in an area and thus affect crime opportunities. The perceived safety derived from street view imagery has propelled scholars to examine its relationship with crime. The literature, however, has not addressed the related geographic scale variability issue; that is, the choice of the geographic analytical units might affect the relationship between area-based perceived safety and crime. This study explores how the relationships between street-view-derived perceived safety and both street thefts and street robberies vary by different spatial scales in Cincinnati. Results of negative binomial models show that perceived safety is positively associated with street thefts and street robberies at both the street segment and census block levels, but is negatively associated with these crimes at the census block group level. The relationship is not statistically significant at the census tract level. This variability is explained by the different freedom of avoidance behaviors in response to perceived safety, which change by geographic scale. The research further evaluates the within variance and between variance of perceived safety at different scales. Compared to between variance, within variance is smaller at both the street segment and block levels, but larger at both the block group and tract levels. This variability can be a source of model instability across multiple geographical scales. In short, the multiscale assessment shows that larger spatial units like the census tract are unsuitable for perceived safety-crime analysis. 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La seguridad percibida del entorno construido-una ponderaci & oacute;n cognitiva diferente del miedo emocional a la criminalidad-podr & iacute;a afectar al n & uacute;mero potencial de v & iacute;ctimas de los criminales en una zona y, de esa manera, afectar las oportunidades de delinquir. La seguridad percibida, que se deriva de la imagener & iacute;a callejera, ha impulsado a los estudiosos a examinar su relaci & oacute;n con la criminalidad. Sin embargo, en la literatura no se han abordado cuestiones relacionadas con la variabilidad de la escala geogr & aacute;fica; es decir, la escogencia de las unidades geogr & aacute;ficas de an & aacute;lisis podr & iacute;a afectar la relaci & oacute;n entre la seguridad percibida basada en zona y la delincuencia. Este estudio explora el modo como las relaciones entre la seguridad percibida, derivada de lo que se ve en las calles, tanto en t & eacute;rminos de hurtos como de atracos callejeros, var & iacute;a en Cincinnati seg & uacute;n las diferentes escalas espaciales. Los resultados de los modelos binomiales negativos muestran que la seguridad percibida est & aacute; positivamente asociada con los hurtos y atracos callejeros, tanto a nivel de segmento de calle como de bloque censal, pero se asocia negativamente con estos delitos a nivel de grupo de bloques censales. La relaci & oacute;n no es estad & iacute;sticamente significativa a nivel de secci & oacute;n censal. Esta variabilidad se explica por la diferente libertad de los comportamientos para evitar la exposici & oacute;n al crimen en respuesta a la seguridad percibida, que cambia seg & uacute;n la escala geogr & aacute;fica. Tambi & eacute;n se eval & uacute;a en la investigaci & oacute;n la varianza interna y la varianza entre las distintas escalas de seguridad percibida. Comparada con la varianza entre & aacute;reas, la varianza dentro de & eacute;stas es menor tanto a nivel de segmento de calle como de manzana, pero mayor tanto a nivel de grupo de manzanas como de tramo. Esta variabilidad puede ser una fuente de inestabilidad del modelo a trav & eacute;s de m & uacute;ltiples escalas geogr & aacute;ficas. En resumen, la evaluaci & oacute;n multiescalar muestra que las unidades espaciales m & aacute;s grandes, como el tracto censal, no son adecuadas para el an & aacute;lisis de la percepci & oacute;n de seguridad y delincuencia.

BackgroundIntegrating optical and LiDAR data is crucial for accurately predicting aboveground biomass (AGB) due to their complementarily essential characteristics. It can be anticipated that this integration approach needs to deal with an expanded set of variables and scale-related challenges. To achieve satisfactory accuracy in real-world applications, further exploration is needed to optimize AGB models by selecting appropriate scales and variables.MethodsThis study examined the impact of LiDAR point cloud-derived metrics on estimation accuracies at different scales, ranging from 2 to 16 m cell sizes. We integrated WorldView-2 imagery with LiDAR data to construct biomass models and developed a genetic algorithm-based wrapper for variable selection and parameter tuning in artificial neural networks (GA-ANN wrapper).ResultsOur findings indicated that the highest accuracies in estimating AGB were yielded by 4 m and 6 m cell sizes, followed by 8 m and 10 m, associated with the dimensions of vegetation canopies and sampling plots. Models integrating WorldView-2 and LiDAR data outperformed those using each data source individually, reducing RMSEr by 5.80% and 3.89%, respectively. Combining these data sources can capture the canopy spectral responses and vertical vegetation structure. The GA-ANN wrapper model decreased RMSEr by 1.69% over the ANN model and dwindled the number of variables from 38 to 9. The selected variables included vegetation density, height, species, and vegetation indices.ConclusionsThe appropriate cell size for AGB estimation should consider the sizes of vegetation canopies, tree densities, and sampling plots. The GA-ANN wrapper effectively reduced variables and achieved the highest accuracy. Additionally, canopy spectral and vertical structure information are vital for accurate AGB estimation. Our study offered insights into optimizing mangrove AGB models by integrating optical and LiDAR data. The approach, data, model, and indices employed in this research can effectively predict AGB estimates of any other forest types or vegetation cover types in different climate regions.

The association between villages in the city (ViCs) and crime is an important topic but has not been rigorously investigated in crime geography. This study developed three new variables in explaining burglary at the community level: the existence of ViCs, the area of ViCs and the ViC-based segregation index. We extracted all ViCs in the study area from satellite images using machine learning. The relationships between these variables and burglary are tested using two negative binomial models. The existence of ViCs in a community is significantly associated with burglary. Results showed that the ViC communities have more burglary than non-ViC communities. Further, the size of ViCs in a community is the most powerful variable in explaining burglary in ViC communities. Communities with larger ViCs have more burglary. This ViC area variable is critical, given that the number of households, a traditional measure of burglary target, is not effective in a city with ViCs. The co-variates pertaining to disadvantaged population, mixture of locals and non-locals, informal controls, crime generators and accessibility shed light on why ViCs contribute to crime. Finally, ViC-based segregation is not significantly related to burglary. This finding is an important addition to the literature on segregation and crime, a challenging research problem that requires future attention. The high concentration of crime in ViCs calls for targeted intervention strategies by law enforcement agencies.

Street theft crime remains a significant public safety concern in China and a central focus of urban crime prevention initiatives. Recent studies have combined street view images and deep learning networks to detect on-street population and streetscape physical environment features, highlighting their significance in comprehending the occurrence of street crime. However, the question of whether on-street population better represents the ‘true risk population of street crime’ compared to previous static (residential) and dynamic (ambient) measures, and whether this advantage persists across different times of the day, remains unexplored. Additionally, the association between streetscape physical environment features and street theft crime, as established in the existing literature, has not been thoroughly examined with respect to temporal variations throughout the day. To address these gaps, this study employed a machine learning model and a simultaneous negative binomial regression model to explore the influence of on-street population and streetscape physical environment on street theft counts. Specifically, we assessed whether their effects exhibit noticeable variations between day and night. Furthermore, we controlled for potential effects of crime attractors, generators, and sociodemographic variables. Our findings demonstrate, firstly, that on-street population extracted from street view images outperforms other measures in assessing the risk population for street theft due to its superior ability to indicate outdoor activities. Moreover, this advantage of on-street population holds true during both daytime and nighttime hours. Secondly, differences in the impact of streetscape physical environment variables and control variables on street theft counts are observed across daytime and nighttime hours. On-street population proves to be a promising alternative for measuring the risk population of street crime victimization, and temporal variations should not be overlooked in future research endeavors.

Different stormwater management practices have been used to mitigate the impacts of urbanization and attenuate urban flooding risk. However, there is a lack of studies on the comparison between conventional stormwater control measures such as detention basins, their retrofits, and low impact development (LID) practices, especially from the perspective of their impacts on erosive flow attenuation and flood control. The aim of this study is to compare the effects of two detention basins and their outlet retrofits with LID, in an urbanized catchment under designed storm events with different return periods, using a Storm Water Management Model (SWMM) modeling approach. The results show that detention basins are effective in decreasing peak flow and delaying peak time. Detention basin retrofits could significantly reduce the frequency and duration of erosive flows in Sub A detention basin compared with LID, especially in smaller rainfall events. LID outperforms the detention basin retrofits in reducing peak flow from larger storms.

The planted alien mangrove species (i.e., Sonneratia apetala, SOA) was deliberately introduced to control the growth and spread of invasive species (i.e., Spartina alterniflora, SA) and restore mangrove ecosystems in Qi'ao Island, the largest artificially planted mangrove reserve of China. However, the effects of the alien species triggered a debate over whether they invaded or restored the mangrove vegetation. There is a gap in observing the impact of planted alien species on invasive alien species and native species over long periods. This study employed high-resolution images with less than 1 m acquired in 2002, 2008, 2010, 2013, and 2016 to examine spatial dynamics and interactions between alien, invasive, and native species. Results showed that mangrove areas increased to 327.56 ha in 2016 from 79.63 ha in 2002, while the presence of the invasive species SA dwindled to 4.19 ha in 2016, down from 110.18 ha in 2002. The increase in mangrove stand areas was mainly due to the artificial planting of SOA. Most of the SA habitat was occupied by SOA from 2002 to 2016, suggesting that artificial SOA can effectively control the growth and extension of invasive SA. There was no notable transfer from native to alien species. This indicated that alien species did not significantly impact the selection of native species in our study area during the observed period. However, we found that the alien species demonstrates adaptability, limiting the expansion of native species. Therefore, we should not underestimate the invasive power of alien species in mangrove ecosystems. Its ecological and biological effects still need to be carefully assessed over an extended period. The findings provided insight into the impact of planted alien mangroves from the perspective of spatiotemporal changes in species compositions.

Sexual crime is a critical global social problem. There remains a critical knowledge gap concerning whether and to what extent sexual crimes in public outdoor spaces can be influenced by landscape morphology of green spaces. This missing knowledge hinders the effective use of green spaces to reduce sexual crimes in these public settings. To address this issue, we collected a dataset comprising 5,155 cases of sexual crimes that occurred in public outdoor spaces in the United States from August 2021 to July 2022. A random forest model was employed to examine the statistical relationships between landscape morphology and sexual crimes. Additionally, we utilized the Shapley Additive Explanations (SHAP) model to quantify the interaction effects of landscape morphology with socioeconomic and demographic characteristics. This study yields three key findings: (1) Both the proportion and configuration factors of landscape morphology may significantly influence the sexual crime probability. (2) The relationships between landscape morphology and sexual crimes are nonlinear, and threshold values for the satisfactory dose and the preferred dose of green spaces can be identified. (3) There are significant interaction effects between landscape morphology with socioeconomic and demographic characteristics, emphasizing the importance of prioritizing green space interventions in socioeconomically disadvantaged areas. Lastly, through summarizing the findings of this study and previous research, we propose the Landscape-Sexual Crime Model (LSCM), which advocates for further research to explore effective strategies for using green spaces to reduce sexual crimes.

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.

Evaluating the rural population hollowing (RPH) caused by the massive out-migration of rural laborers is essential for China’s rural land use policy formulation and rural revitalization. Existing studies mainly assessed the RPH based on survey data or statistical data. The former is mainly based on selected local villages and towns, and cannot be replicated on a large scale, while the latter is usually at the county or city scale and cannot reveal the cyclical migration of rural labors at fine spatial resolutions such as townships. To fill this gap, this paper proposed a social media data based-rural population hollowing (SM-RPH) index, which was calculated by Tencent User Density big data on holidays and non-holidays and was used to characterize the "migratory bird" process of rural laborers. Taking Guangdong Province, China as the case, this study employed the proposed novel index to evaluate the RPH at the county and township scales. This was integrated with a GWR model to explore how the important contributing factors of RPH vary across the province. The results showed a trend of transition from the cold spot areas in the core regions of PRD to the hot spot areas around the PRD. Additionally, this study revealed that distance to the nearest prefecture-level city, total nighttime lighting per capita, fiscal expenditure per unit of GDP and road network density were dominant factors in affecting rural population hollowing, and the influence of these four variables vary considerably throughout the province. Further, k-means cluster was used to group the hollowing towns according to the local coefficients of the influencing factors, and policy zoning was suggested to provide differentiated countermeasures and suggestions for rural revitalization. This methodological framework can be easily applied to other regions, and is expected to be used to evaluate the return characteristics of migrant workers in real time to help monitor the progress of rural revitalization in China and provide decision support for policy makers.