Prof. Zengli Wang
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Representative Publications
View All Publications13 March 2020 in International Journal of Geographical Information Science
Journal Article
BYBo Yang
Bo Yang
Lin LiuMLMinxuan Lan
Minxuan LanZengli WangHZHanlin Zhou
Hanlin ZhouHYHongjie Yu
Hongjie Yu
Accurate crime prediction can help allocate police resources for crime reduction and prevention. There are two popular approaches to predict criminal activities: one is based on historical crime, and the other is based on environmental variables correlated with criminal patterns. Previous research on geo-statistical modeling mainly considered one type of data in space-time domain, and few sought to blend multi-source data. In this research, we proposed a spatio-temporal Cokriging algorithm to integrate historical crime data and urban transitional zones for more accurate crime prediction. Time-series historical crime data were used as the primary variable, while urban transitional zones identified from the VIIRS nightlight imagery were used as the secondary co-variable. The algorithm has been applied to predict weekly-based street crime and hotspots in Cincinnati, Ohio. Statistical tests and Predictive Accuracy Index (PAI) and Predictive Efficiency Index (PEI) tests were used to validate predictions in comparison with those of the control group without using the co-variable. The validation results demonstrate that the proposed algorithm with historical crime data and urban transitional zones increased the correlation coefficient by 5.4% for weekdays and by 12.3% for weekends in statistical tests, and gained higher hit rates measured by PAI/PEI in the hotspots test.
Journal Article
MLMinxuan Lan
Minxuan LanLin LiuAHAndres Hernandez
Andres HernandezWLWeiyi Liu
Weiyi LiuHZHanlin Zhou
Hanlin ZhouZengli WangAs a measurement of the residential population, the Census population ignores the mobility of the people. This weakness may be alleviated by the use of ambient population, derived from social media data such as tweets. This research aims to examine the degree in which geotagged tweets, in contrast to the Census population, can explain crime. In addition, the mobility of Twitter users suggests that tweets as the ambient population may have a spillover effect on the neighboring areas. Based on a yearlong geotagged tweets dataset, negative binomial regression models are used to test the impact of tweets derived ambient population, as well as its possible spillover effect on theft crimes. Results show: (1) Tweets count is a viable replacement of the Census population for spatial theft pattern analysis; (2) tweets count as a measure of the ambient population shows a significant spillover effect on thefts, while such spillover effect does not exist for the Census population; (3) the combination of tweets and its spatial lag outperforms the Census population in theft crime analyses. Therefore, the spillover effect of the tweets derived ambient population should be considered in future crime analyses. This finding may be applicable to other social media data as well.
Journal Article
Zengli WangXLXuejun Liu
Xuejun LiuA hot spot refers to numerous crime incidents clustered in a limited space-time range. The near-repeat phenomenon suggests that every victimization might form a contagion-like pattern nearby in terms of both space and time. In this article, the near-repeat phenomenon is used to analyze the risk levels around hot spots. Utilizing a recent burglary dataset in N (a large city located in southeastern China), we examine the near-repeat phenomenon, the results of which we then use to test the contributions of hot spots. More importantly, we propose a temporal expanded near-repeat matrix to quantify the undulation of risk both before and after hot spots. The experimental results demonstrate that hot spots always form. Space-time areas of high risk are always variable in space and time. Regions in the vicinity of hot spots simultaneously share this higher risk. In general, crime risks around hot spots present as a wave diffusion process. The conclusions herein provide a detailed analysis of criminal patterns, which not only advances previous results but also provides valuable research results for crime prediction and prevention.
Journal Article
HZHanlin Zhou
Hanlin ZhouLin LiuMLMinxuan Lan
Minxuan LanBYBo Yang
Bo YangZengli WangPrevious research has recognized the importance of edges to crime. Various scholars have explored how one specific type of edges such as physical edges or social edges affect crime, but rarely investigated the importance of the composite edge effect. To address this gap, this study introduces nightlight data from the Visible Infrared Imaging Radiometer Suite sensor on the Suomi National Polar-orbiting Partnership Satellite (NPP-VIIRS) to measure composite edges. This study defines edges as nightlight gradients—the maximum change of nightlight from a pixel to its neighbors. Using nightlight gradients and other control variables at the tract level, this study applies negative binomial regression models to investigate the effects of edges on the street robbery rate and the burglary rate in Cincinnati. The Akaike Information Criterion (AIC) of models show that nightlight gradients improve the fitness of models of street robbery and burglary. Also, nightlight gradients make a positive impact on the street robbery rate whilst a negative impact on the burglary rate, both of which are statistically significant under the alpha level of 0.05. The different impacts on these two types of crimes may be explained by the nature of crimes and the in-situ characteristics, including nightlight.
1 December 2020 in Applied Geography
Journal Article
18 January 2021 in Urban Studies
Journal Article
Zengli WangLin LiuCHCory Haberman
Cory Haberman
MLMinxuan Lan
Minxuan LanBYBo Yang
Bo Yang
HZHanlin Zhou
Hanlin Zhou
This article examines whether different levels of entry controls impact burglary rates in gated communities. It differs from the previous studies that only distinguish gated communities from non-gated communities but ignore important variation in different levels of entry controls. A sample of 698 gated communities in a large Chinese city are selected for this study. A negative binomial regression model estimates the relationships between entry control levels and burglary rates in gated communities. The test of these relationships accounts for the control of other important explanatory variables, including management fee, building height, building age, housing price, house for sale, rental house and floating population. Results indicate that higher entry control levels are associated with significantly lower burglary rates in gated communities. This is the first study that reveals a quantitative relationship between burglary and entry control level in gated communities at the city-wide scale.
About Me
Dr. Zengli Wang is an Associate Professor, Master’s Supervisor, and National Registered Surveying and Mapping Engineer at Hohai University, where he also serves as the Branch Secretary of the Department of Remote Sensing. He earned his Ph.D. in Cartography and GIS from Nanjing Normal University and conducted postdoctoral research at the University of Cincinnati from 2017 to 2019. A distinguished educator, Dr. Wang was honored as a Hohai University Outstanding Chief Lecturer (2026) and has received the Grand Prize in the National Young Teachers’ Teaching Competition for Surveying and Mapping (2022). His dedication to mentorship is further reflected in his leadership of four national and provincial-level Innovation and Entrepreneurship Training Programs for university students.
Dr. Wang’s research focuses on GIS modeling and analysis, geospatial big data, and public safety analytics. He has led several high-level projects, including those funded by the National Natural Science Foundation of China (General and Youth programs) and the Ministry of Education’s Humanities and Social Sciences Fund, addressing critical issues such as the impact of urban renewal on crime mechanisms and the propagation of property-related crimes. With over 20 publications in prestigious journals like IJGIS, IJDE, and Urban Studies, his recent work tackles geocoding optimization and the integration of multi-source spatial data for urban governance.