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Continued settlement monitoring and modeling of landfills are critical for land redevelopment and safety assurance. This paper adopts a MTInSAR technique for time-series monitoring of the Xingfeng landfill (XFL) settlement. A major challenge is that the frequent and significant settlement in the initial stage after the closure of landfills can affect the coherence of interferograms, thus hindering the monitoring of settlement by MTInSAR. We analyzed the factors that can directly affect the temporal decorrelation of landfills and adopted a 3D phase unwrapping approach to correct the phase unwrapping errors caused by such deformation gradient. SAR images from four platforms, including 50 Sentinel-1A, 12 Radarsat-2, 4 ALOS-2, and 2 TerraSAR-X/TanDEM-X images, are collected to measure the settlement and thickness of the landfill. The settlement accuracy is evaluated by a cross-evaluation between Radarsat-2 and Sentinel-1A that have similar temporal coverages. We analyzed the spatial characteristics of settlement and the relationship between the settlement and thickness. Further, we modeled the future settlement of the XFL with a hyperbolic function model. The results showed that the coherence in the initial stage after closure of the XFL is primarily affected by temporal decorrelation caused by considerable deformation gradient compared with spatial decorrelation. Settlement occurs primarily in the forward slope of the XFL, and the maximum line-of-sight (LOS) settlement rate reached 0.808 m/year from August 2018 to May 2020. The correlation between the settlement and thickness is 0.62, indicating an obvious relationship between the two. In addition, the settlement of younger areas is usually greater than that of older areas.
Yanan Du; Haiqiang Fu; Lin Liu; Guangcai Feng; Debao Wen; Xing Peng; Huaxiang Ding. Continued Monitoring and Modeling of Xingfeng Solid Waste Landfill Settlement, China, Based on Multiplatform SAR Images. Remote Sensing 2021, 13, 3286 .
AMA StyleYanan Du, Haiqiang Fu, Lin Liu, Guangcai Feng, Debao Wen, Xing Peng, Huaxiang Ding. Continued Monitoring and Modeling of Xingfeng Solid Waste Landfill Settlement, China, Based on Multiplatform SAR Images. Remote Sensing. 2021; 13 (16):3286.
Chicago/Turabian StyleYanan Du; Haiqiang Fu; Lin Liu; Guangcai Feng; Debao Wen; Xing Peng; Huaxiang Ding. 2021. "Continued Monitoring and Modeling of Xingfeng Solid Waste Landfill Settlement, China, Based on Multiplatform SAR Images." Remote Sensing 13, no. 16: 3286.
The orbit error caused by the inaccuracy of the orbit state vector can lead to fringes in differential interferograms, which can impede the estimation of deformation in differential SAR interferometry (DInSAR) applications. Usually, a set of polynomial coefficients for an entire SAR image is obtained for orbit error removal. However, the orbit error plane is influenced by overfitting in the case that the SAR satellites do not have a precise orbit. In this paper, a patch-based polynomial method is proposed to fit the orbit error plane. The new method divides an SAR image into several overlapping patches in the azimuth and range directions. Every patch obtains its own polynomial coefficients, and an iterative least-square method is used to mosaic the orbit plane. This method is tested and validated via a simulated dataset and then applied to ALOS1/2 PALSAR and Sentinel-1A datasets. The accuracy of deformation is evaluated by in situ GPS datasets. The results show that the patch-based method can fit the orbit phase plane more accurately than the traditional polynomial model with millimeter-level displacement improvement, especially in the margin areas of ALOS1/2 and for the wide-coverage Sentinel-1A datasets. Moreover, in the MTInSAR parameter calculations, the new method improves the accuracy of mean velocity calculations for ALOS1 time series, with a reduction of RMSE from 4.47 mm/yr to 3.17 mm/yr. Additionally, the new method reduces the spatial correlation of the residual topographic phase, with a mean value reduction from 0.32 m to 0.13 m.
Yanan Du; Haiqiang Fu; Lin Liu; Guangcai Feng; Xing Peng; Debao Wen. Orbit error removal in InSAR/MTInSAR with a patch-based polynomial model. International Journal of Applied Earth Observation and Geoinformation 2021, 102, 102438 .
AMA StyleYanan Du, Haiqiang Fu, Lin Liu, Guangcai Feng, Xing Peng, Debao Wen. Orbit error removal in InSAR/MTInSAR with a patch-based polynomial model. International Journal of Applied Earth Observation and Geoinformation. 2021; 102 ():102438.
Chicago/Turabian StyleYanan Du; Haiqiang Fu; Lin Liu; Guangcai Feng; Xing Peng; Debao Wen. 2021. "Orbit error removal in InSAR/MTInSAR with a patch-based polynomial model." International Journal of Applied Earth Observation and Geoinformation 102, no. : 102438.
Video satellite can generate video image sequences with rich dynamic information, thus providing a new way for monitoring moving objects. However, to maintain high temporal resolution, video satellite images usually sacrifice their spatial resolution. Therefore, super-resolution (SR) plays a vital role in improving the quality of video satellite images. In this article, we propose a multiframe video SR neural network (MVSRnet) for video satellite image SR reconstruction. The proposed MVSRnet consists of three main subnetworks: an optical flow estimation subnetwork (OFEnet), an upscaling subnetwork (Upnet) and an attention-based residual learning subnetwork (ARLnet). The OFEnet aims to estimate low-resolution (LR) optical flow of multiple image frames. Upnet is then constructed to enhance the resolution of both input frames and the estimated LR optical flows. Motion compensation is subsequently performed according to the high-resolution (HR) optical flows. Finally, the compensated HR cube is fed to the ARLnet to generate SR results. Different from existing video satellite image SR methods, the proposed MVSRnet is a multiframe-based method with an attention mechanism, which can merge the motion information among adjacent frames and highlight the importance of extracted features. Experiments conducted on Jilin-1 and OVS-1 video satellite images demonstrate that the proposed MVSRnet significantly outperforms some state-of-the-art SR methods.
Zhi He; Jun Li; Lin Liu; Dan He; Man Xiao. Multiframe Video Satellite Image Super-Resolution via Attention-Based Residual Learning. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -15.
AMA StyleZhi He, Jun Li, Lin Liu, Dan He, Man Xiao. Multiframe Video Satellite Image Super-Resolution via Attention-Based Residual Learning. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-15.
Chicago/Turabian StyleZhi He; Jun Li; Lin Liu; Dan He; Man Xiao. 2021. "Multiframe Video Satellite Image Super-Resolution via Attention-Based Residual Learning." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-15.
Understanding how offenders choose a crime location is a classic criminological topic. However, previous research on offenders' crime location choice did not consider the impacts of ambient population and surveillance cameras on street robbery. Based on the literature, this study integrates ambient population and surveillance cameras data, from the perspective of guardianship. The discrete spatial choice modeling is used to test the impact of their guardianship role on street robbers' crime location choice, accounting for accessibility and proximity, crime attractors and generators, and social disorganization. The results demonstrate that ambient population and surveillance cameras have a significant hindering impact on street robbers' crime location choice, and they play a guardianship role in street robbers' criminal activities. In particular, we find that the guardianship effect of ambient population is greater than that of surveillance cameras. Further, the inclusion of ambient population and surveillance cameras increases the fitness of the model, which underscores the guardianship role of these two factors on street robbers' choice of location on committing a robbery. These findings can have important implications for the role of the ambient population and the deployment of surveillance cameras for crime reduction.
Dongping Long; Lin Liu; Mingen Xu; Jiaxin Feng; Jianguo Chen; Li He. Ambient population and surveillance cameras: The guardianship role in street robbers' crime location choice. Cities 2021, 115, 103223 .
AMA StyleDongping Long, Lin Liu, Mingen Xu, Jiaxin Feng, Jianguo Chen, Li He. Ambient population and surveillance cameras: The guardianship role in street robbers' crime location choice. Cities. 2021; 115 ():103223.
Chicago/Turabian StyleDongping Long; Lin Liu; Mingen Xu; Jiaxin Feng; Jianguo Chen; Li He. 2021. "Ambient population and surveillance cameras: The guardianship role in street robbers' crime location choice." Cities 115, no. : 103223.
The importance of combining spatial and temporal aspects has been increasingly recognized over recent years, yet pertinent pattern analysis methods in place-based crime research still need further development to explicitly indicate spatial-temporal localities of pertinent factors’ influence ranges. This paper proposes an approach, Spatial-Temporal Indication of Crime Association (STICA), to facilitate identifying the main contributing factors of crime, which are operated at diverse spatial-temporal scales. The method’s rationale is to progressively discern the spatial zones with diverse temporal crime patterns. A specific implementation of the STICA approach, by combining kernel density estimation, k-median-centers clustering, and thematic mapping, is applied to understand the burglary in an urban peninsula, China. The empirical findings include: (1) both the main time-stable and time-varying factors of crime can be indicated with the disparities of temporal crime patterns for different spatial zones based on the STICA results. (2) The spatial range of these factors can enlighten the understanding of interactions for generating crime patterns, especially with regards to how temporally transient and spatially global factors can produce a locally crime-ridden zone through the mediation of stable factors. (3) The STICA results can reveal the spatially contextual effects of stable factors, which are of great value to improve modeling crime patterns. As demonstrated, the STICA approach is effective in exploring contributing factors of crime and has shown great potential for providing a new vision in place-based crime research.
Chao Jiang; Lin Liu; Xiaoxing Qin; Suhong Zhou; Kai Liu. Discovering Spatial-Temporal Indication of Crime Association (STICA). ISPRS International Journal of Geo-Information 2021, 10, 67 .
AMA StyleChao Jiang, Lin Liu, Xiaoxing Qin, Suhong Zhou, Kai Liu. Discovering Spatial-Temporal Indication of Crime Association (STICA). ISPRS International Journal of Geo-Information. 2021; 10 (2):67.
Chicago/Turabian StyleChao Jiang; Lin Liu; Xiaoxing Qin; Suhong Zhou; Kai Liu. 2021. "Discovering Spatial-Temporal Indication of Crime Association (STICA)." ISPRS International Journal of Geo-Information 10, no. 2: 67.
This research aims to analyze CCD inpatients' mobility patterns in central China and explore the influences of nonclinical factors on their decisions of medical travel. Using patients' information (n = 105,120) collected from Henan, a central province of China, as well as county-level health care, transportation, and socioeconomic data, we analyzed spatial-temporal variations of CCD inpatients' mobility from 2013 to 2017. The multiple regression, geographically weighted regression, and path analysis were integrated to reveal how health care level (availability), insurance coverage (affordability), transportation/accessibility, local economic development, and regional factors have influenced CCD inpatients' medical travel. Our findings indicate that CCD patients’ mobility has greatly increased in central China, with a growth rate of 28.9% from 2013 to 2017. The spatial pattern of medical travel was uneven among four economic zones but relatively balanced between urban rural areas. Over 51% of CCD inpatients traveled from the most disadvantaged zone to the capital city for treatment. The mobility pattern has been mainly determined by local medical resources, health insurance coverage, and transportation/accessibility. Furthermore, despite little direct effects, local economic development and regional factors have greatly influenced medical travel through indirectly affecting the local health care sector and transportation development.
Yingru Li; Lin Liu; Jianguo Chen; Jiewen Zhang. Medical travel of cardiovascular and cerebrovascular diseases inpatients in central China. Applied Geography 2021, 127, 102391 .
AMA StyleYingru Li, Lin Liu, Jianguo Chen, Jiewen Zhang. Medical travel of cardiovascular and cerebrovascular diseases inpatients in central China. Applied Geography. 2021; 127 ():102391.
Chicago/Turabian StyleYingru Li; Lin Liu; Jianguo Chen; Jiewen Zhang. 2021. "Medical travel of cardiovascular and cerebrovascular diseases inpatients in central China." Applied Geography 127, no. : 102391.
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.
Zengli Wang; Lin Liu; Cory Haberman; Minxuan Lan; Bo Yang; Hanlin Zhou. Burglaries and entry controls in gated communities. Urban Studies 2021, 1 .
AMA StyleZengli Wang, Lin Liu, Cory Haberman, Minxuan Lan, Bo Yang, Hanlin Zhou. Burglaries and entry controls in gated communities. Urban Studies. 2021; ():1.
Chicago/Turabian StyleZengli Wang; Lin Liu; Cory Haberman; Minxuan Lan; Bo Yang; Hanlin Zhou. 2021. "Burglaries and entry controls in gated communities." Urban Studies , no. : 1.
Previous literature has examined the relationship between the amount of green space and perceived safety in urban areas, but little is known about the effect of street-view neighborhood greenery on perceived neighborhood safety. Using a deep learning approach, we derived greenery from a massive set of street view images in central Guangzhou. We further tested the relationships and mechanisms between street-view greenery and fear of crime in the neighborhood. Results demonstrated that a higher level of neighborhood street-view greenery was associated with a lower fear of crime, and its relationship was mediated by perceived physical incivilities. While increasing street greenery of the micro-environment may reduce fear of crime, this paper also suggests that social factors should be considered when designing ameliorative programs.
Fengrui Jing; Lin Liu; Suhong Zhou; Jiangyu Song; Linsen Wang; Hanlin Zhou; Yiwen Wang; Ruofei Ma. Assessing the Impact of Street-View Greenery on Fear of Neighborhood Crime in Guangzhou, China. International Journal of Environmental Research and Public Health 2021, 18, 311 .
AMA StyleFengrui Jing, Lin Liu, Suhong Zhou, Jiangyu Song, Linsen Wang, Hanlin Zhou, Yiwen Wang, Ruofei Ma. Assessing the Impact of Street-View Greenery on Fear of Neighborhood Crime in Guangzhou, China. International Journal of Environmental Research and Public Health. 2021; 18 (1):311.
Chicago/Turabian StyleFengrui Jing; Lin Liu; Suhong Zhou; Jiangyu Song; Linsen Wang; Hanlin Zhou; Yiwen Wang; Ruofei Ma. 2021. "Assessing the Impact of Street-View Greenery on Fear of Neighborhood Crime in Guangzhou, China." International Journal of Environmental Research and Public Health 18, no. 1: 311.
Based on an offender spatial decision-making perspective, this burglary target location choice study aims to understand how physical and social barriers affect why residential burglars commit their crimes at particular locations in a major Chinese city. Using data on 3860 residential burglaries committed by 3772 burglars between January 2012 and June 2016 in ZG city, China, conditional logit (discrete choice) models were estimated to assess residential burglars' target location choice preferences. Three types of physical barriers were distinguished: major roads with access control, major roads without access control, and major rivers. Social barriers were constructed based on the Hukou system to reflect how local and nonlocal residents live segregated lives. Results show that residential burglars are less likely to target areas for which they have to cross a physical barrier and even less likely to do so if they have to cross multiple rivers. Local burglars are more likely to target communities with a majority of local residents than communities with a majority nonlocal population or a mixed community. Such a social barrier was less pronounced for nonlocal burglars. These findings add new insight that physical and social barriers affect, to various degrees, where residential burglars in China commit their crimes.
Luzi Xiao; Stijn Ruiter; Lin Liu; Guangwen Song; Suhong Zhou. Burglars blocked by barriers? The impact of physical and social barriers on residential burglars' target location choices in China. Computers, Environment and Urban Systems 2020, 86, 101582 .
AMA StyleLuzi Xiao, Stijn Ruiter, Lin Liu, Guangwen Song, Suhong Zhou. Burglars blocked by barriers? The impact of physical and social barriers on residential burglars' target location choices in China. Computers, Environment and Urban Systems. 2020; 86 ():101582.
Chicago/Turabian StyleLuzi Xiao; Stijn Ruiter; Lin Liu; Guangwen Song; Suhong Zhou. 2020. "Burglars blocked by barriers? The impact of physical and social barriers on residential burglars' target location choices in China." Computers, Environment and Urban Systems 86, no. : 101582.
Crime prediction using machine learning and data fusion assimilation has become a hot topic. Most of the models rely on historical crime data and related environment variables. The activity of potential offenders affects the crime patterns, but the data with fine resolution have not been applied in the crime prediction. The goal of this study is to test the effect of the activity of potential offenders in the crime prediction by combining this data in the prediction models and assessing the prediction accuracies. This study uses the movement data of past offenders collected in routine police stop-and-question operations to infer the movement of future offenders. The offender movement data compensates historical crime data in a Spatio-Temporal Cokriging (ST-Cokriging) model for crime prediction. The models are implemented for weekly, biweekly, and quad-weekly prediction in the XT police district of ZG city, China. Results with the incorporation of the offender movement data are consistently better than those without it. The improvement is most pronounced for the weekly model, followed by the biweekly model, and the quad-weekly model. In sum, the addition of offender movement data enhances crime prediction, especially for short periods.
Hongjie Yu; Lin Liu; Bo Yang; Minxuan Lan. Crime Prediction with Historical Crime and Movement Data of Potential Offenders Using a Spatio-Temporal Cokriging Method. ISPRS International Journal of Geo-Information 2020, 9, 732 .
AMA StyleHongjie Yu, Lin Liu, Bo Yang, Minxuan Lan. Crime Prediction with Historical Crime and Movement Data of Potential Offenders Using a Spatio-Temporal Cokriging Method. ISPRS International Journal of Geo-Information. 2020; 9 (12):732.
Chicago/Turabian StyleHongjie Yu; Lin Liu; Bo Yang; Minxuan Lan. 2020. "Crime Prediction with Historical Crime and Movement Data of Potential Offenders Using a Spatio-Temporal Cokriging Method." ISPRS International Journal of Geo-Information 9, no. 12: 732.
Fear of crime can lead to lower satisfaction with life and subjective well-being. The indicators of fear of crime vary from the social and cultural context, and the hukou (household registration) status causes unequal rights between local hukou and non-local hukou residents in China. To improve people’s perception of safety, this study takes hukou as an indicator of social vulnerability and examines the relationship between hukou, perceived neighborhood conditions, and fear of crime in China. A binary logistic regression model was used to analyze the 1727 residents garnered from the 2016 Project on Public Safety in Guangzhou Neighborhoods (PPSGN) in Guangzhou, China. The results show that women, victimization experience, physical and social disorder, and neighborhood policing are associated with residents’ fear of crime. Although hukou status has no statistically significant effect on fear of crime, hukou status significantly moderates the influence of perceived neighborhood conditions on fear of crime. That is, perceived neighborhood conditions’ effects on fear are conditional on one’s hukou status: non-local hukou, perception of the social disorder has more of the detrimental effect on fear, and perception of social integration has less of the helpful effect on fear. In sum, this study adds to the international literature by revealing the conditional effect of the hukou on fear in a Chinese city.
Fengrui Jing; Lin Liu; Suhong Zhou; Guangwen Song. Examining the Relationship between Hukou Status, Perceived Neighborhood Conditions, and Fear of Crime in Guangzhou, China. Sustainability 2020, 12, 9614 .
AMA StyleFengrui Jing, Lin Liu, Suhong Zhou, Guangwen Song. Examining the Relationship between Hukou Status, Perceived Neighborhood Conditions, and Fear of Crime in Guangzhou, China. Sustainability. 2020; 12 (22):9614.
Chicago/Turabian StyleFengrui Jing; Lin Liu; Suhong Zhou; Guangwen Song. 2020. "Examining the Relationship between Hukou Status, Perceived Neighborhood Conditions, and Fear of Crime in Guangzhou, China." Sustainability 12, no. 22: 9614.
This study examines the macro and micro impacts of a casino on multiple crime types over time. JACK Casino, opened on March 4, 2013, is near the Central Business District of Cincinnati, Ohio. We use the weighted displacement quotient and a series of negative binomial models for the years from 2010 to 2016 to compare before-and-after crime patterns within the neighboring area of the casino (within 400-meter) compared to the entire city. Results show that the casino has differing effects on property and violent crime in regard to crime density and spatial patterns. Within the casino's neighboring area, property crime density decreased in the year of construction (2012) and the year of opening (2013), but increased in the following years (2014–2016). At the same time, the city experienced an overall decline in property crime. Also, property crime incidents started to cluster around the casino after its opening. We confirmed this distance decay by statistical analyses at both macro (citywide) and micro (neighboring area) scales. In contrast, the casino did not show such an obvious impact on violent crime. The initial increase of violent crime density just after the casino opened was followed by a drop to the pre-casino level. Meanwhile, although violent crime patterns around the casino were slightly altered because of the casino, the change was not statistically significant. The difference between property and violent crime in response to the casino's opening is an important contribution to the literature. Our findings also demonstrate that it is vital to examine the micro-spatial and temporal impact of casinos, rather than rely on the cross-sectional examination of jurisdiction-wide crime levels. Further, this approach should be generally applicable to other regions.
Minxuan Lan; Lin Liu; John E. Eck. A spatial analytical approach to assess the impact of a casino on crime: An example of JACK Casino in downtown Cincinnati. Cities 2020, 111, 103003 .
AMA StyleMinxuan Lan, Lin Liu, John E. Eck. A spatial analytical approach to assess the impact of a casino on crime: An example of JACK Casino in downtown Cincinnati. Cities. 2020; 111 ():103003.
Chicago/Turabian StyleMinxuan Lan; Lin Liu; John E. Eck. 2020. "A spatial analytical approach to assess the impact of a casino on crime: An example of JACK Casino in downtown Cincinnati." Cities 111, no. : 103003.
Accurate methods to estimate the aboveground biomass (AGB) of mangroves are required to monitor the subtle changes over time and assess their carbon sequestration. The AGB of forests is a function of canopy-related information (canopy density, vegetation status), structures, and tree heights. However, few studies have attended to integrating these factors to build models of the AGB of mangrove plantations. The objective of this study was to develop an accurate and robust biomass estimation of mangrove plantations using Chinese satellite optical, SAR, and Unmanned Aerial Vehicle (UAV) data based digital surface models (DSM). This paper chose Qi’ao Island, which forms the largest contiguous area of mangrove plantation in China, as the study area. Several field visits collected 127 AGB samples. The models for AGB estimation were developed using the random forest algorithm and integrating images from multiple sources: optical images from Gaofen-2 (GF-2), synthetic aperture radar (SAR) images from Gaofen-3 (GF-3), and UAV-based digital surface model (DSM) data. The performance of the models was assessed using the root-mean-square error (RMSE) and relative RMSE (RMSEr), based on five-fold cross-validation and stratified random sampling approach. The results showed that images from the GF-2 optical (RMSE = 33.49 t/ha, RMSEr = 21.55%) or GF-3 SAR (RMSE = 35.32 t/ha, RMSEr = 22.72%) can be used appropriately to monitor the AGB of the mangrove plantation. The AGB models derived from a combination of the GF-2 and GF-3 datasets yielded a higher accuracy (RMSE = 29.89 t/ha, RMSEr = 19.23%) than models that used only one of them. The model that used both datasets showed a reduction of 2.32% and 3.49% in RMSEr over the GF-2 and GF-3 models, respectively. On the DSM dataset, the proposed model yielded the highest accuracy of AGB (RMSE = 25.69 t/ha, RMSEr = 16.53%). The DSM data were identified as the most important variable, due to mitigating the saturation effect observed in the optical and SAR images for a dense AGB estimation of the mangroves. The resulting map, derived from the most accurate model, was consistent with the results of field investigations and the mangrove plantation sequences. Our results indicated that the AGB can be accurately measured by integrating images from the optical, SAR, and DSM datasets to adequately represent canopy-related information, forest structures, and tree heights.
Yuanhui Zhu; Kai Liu; Soe W. Myint; Zhenyu Du; Yubin Li; Jingjing Cao; Lin Liu; Zhifeng Wu. Integration of GF2 Optical, GF3 SAR, and UAV Data for Estimating Aboveground Biomass of China’s Largest Artificially Planted Mangroves. Remote Sensing 2020, 12, 2039 .
AMA StyleYuanhui Zhu, Kai Liu, Soe W. Myint, Zhenyu Du, Yubin Li, Jingjing Cao, Lin Liu, Zhifeng Wu. Integration of GF2 Optical, GF3 SAR, and UAV Data for Estimating Aboveground Biomass of China’s Largest Artificially Planted Mangroves. Remote Sensing. 2020; 12 (12):2039.
Chicago/Turabian StyleYuanhui Zhu; Kai Liu; Soe W. Myint; Zhenyu Du; Yubin Li; Jingjing Cao; Lin Liu; Zhifeng Wu. 2020. "Integration of GF2 Optical, GF3 SAR, and UAV Data for Estimating Aboveground Biomass of China’s Largest Artificially Planted Mangroves." Remote Sensing 12, no. 12: 2039.
Drug addiction and drug-related crime caused by drug dealing are serious problems for many countries. Such problems have gained urgency in China during recent years. However, there has been no research on the relationship between drug dealing and associated factors and its variation over space at a fine scale, such as the police station management area (PSMA), in China. Based on a seven-year data set obtained in ZG city, China, a geographically weighted Poisson regression (GWPR) model was employed to explore the spatial heterogeneity in the relationship between drug dealing and related risk factors, including social-demographic factors and environmental characteristics. The model results indicated that there were more drug dealings in the socially disorganized areas, typically associated with urban villages and floating population. Spatial accessibility had significant impacts on drug dealing. While the main road showed a negative effect, areas with more branch roads and bus stops tended to attract more drug dealings. Additionally, we found that these relationships were spatially nonstationary across space. This research represents the first in discerning spatial variation of drug dealing within a major Chinese city. These findings not only help policy makers better understand drug dealings from a geographical perspective, but can also help them to develop more targeted local intervention strategies.
Jianguo Chen; Lin Liu; Huiting Liu; Dongping Long; Chong Xu; Hanlin Zhou. The Spatial Heterogeneity of Factors of Drug Dealing: A Case Study from ZG, China. ISPRS International Journal of Geo-Information 2020, 9, 205 .
AMA StyleJianguo Chen, Lin Liu, Huiting Liu, Dongping Long, Chong Xu, Hanlin Zhou. The Spatial Heterogeneity of Factors of Drug Dealing: A Case Study from ZG, China. ISPRS International Journal of Geo-Information. 2020; 9 (4):205.
Chicago/Turabian StyleJianguo Chen; Lin Liu; Huiting Liu; Dongping Long; Chong Xu; Hanlin Zhou. 2020. "The Spatial Heterogeneity of Factors of Drug Dealing: A Case Study from ZG, China." ISPRS International Journal of Geo-Information 9, no. 4: 205.
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.
Bo Yang; Lin Liu; Minxuan Lan; Zengli Wang; Hanlin Zhou; Hongjie Yu. A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery. International Journal of Geographical Information Science 2020, 34, 1740 -1764.
AMA StyleBo Yang, Lin Liu, Minxuan Lan, Zengli Wang, Hanlin Zhou, Hongjie Yu. A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery. International Journal of Geographical Information Science. 2020; 34 (9):1740-1764.
Chicago/Turabian StyleBo Yang; Lin Liu; Minxuan Lan; Zengli Wang; Hanlin Zhou; Hongjie Yu. 2020. "A spatio-temporal method for crime prediction using historical crime data and transitional zones identified from nightlight imagery." International Journal of Geographical Information Science 34, no. 9: 1740-1764.
Negative binomial (NB) regression model has been used to analyze crime in previous studies. The disadvantage of the NB model is that it cannot deal with spatial effects. Therefore, spatial regression models, such as the geographically weighted Poisson regression (GWPR) model, were introduced to address spatial heterogeneity in crime analysis. However, GWPR could not account for overdispersion, which is commonly observed in crime data. The geographically weighted negative binomial model (GWNBR) was adopted to address spatial heterogeneity and overdispersion simultaneously in crime analysis, based on a 3-year data set collected from ZG city, China, in this study. The count of residential burglaries was used as the dependent variable to calibrate the above models, and the results revealed that the GWPR and GWNBR models performed better than NB for reducing spatial dependency in the model residuals. GWNBR outperformed GWPR for incorporating overdispersion. Therefore, GWNBR was proven to be a promising tool for crime modeling.
Jianguo Chen; Lin Liu; Luzi Xiao; Chong Xu; Dongping Long. Integrative Analysis of Spatial Heterogeneity and Overdispersion of Crime with a Geographically Weighted Negative Binomial Model. ISPRS International Journal of Geo-Information 2020, 9, 60 .
AMA StyleJianguo Chen, Lin Liu, Luzi Xiao, Chong Xu, Dongping Long. Integrative Analysis of Spatial Heterogeneity and Overdispersion of Crime with a Geographically Weighted Negative Binomial Model. ISPRS International Journal of Geo-Information. 2020; 9 (1):60.
Chicago/Turabian StyleJianguo Chen; Lin Liu; Luzi Xiao; Chong Xu; Dongping Long. 2020. "Integrative Analysis of Spatial Heterogeneity and Overdispersion of Crime with a Geographically Weighted Negative Binomial Model." ISPRS International Journal of Geo-Information 9, no. 1: 60.
Coastal areas are usually densely populated, economically developed, ecologically dense, and subject to a phenomenon that is becoming increasingly serious, land subsidence. Land subsidence can accelerate the increase in relative sea level, lead to a series of potential hazards, and threaten the stability of the ecological environment and human lives. In this paper, we adopted two commonly used multi-temporal interferometric synthetic aperture radar (MTInSAR) techniques, Small baseline subset (SBAS) and Temporarily coherent point (TCP) InSAR, to monitor the land subsidence along the entire coastline of Guangdong Province. The long-wavelength L-band ALOS/PALSAR-1 dataset collected from 2007 to 2011 is used to generate the average deformation velocity and deformation time series. Linear subsidence rates over 150 mm/yr are observed in the Chaoshan Plain. The spatiotemporal characteristics are analyzed and then compared with land use and geology to infer potential causes of the land subsidence. The results show that (1) subsidence with notable rates (>20 mm/yr) mainly occurs in areas of aquaculture, followed by urban, agricultural, and forest areas, with percentages of 40.8%, 37.1%, 21.5%, and 0.6%, respectively; (2) subsidence is mainly concentrated in the compressible Holocene deposits, and clearly associated with the thickness of the deposits; and (3) groundwater exploitation for aquaculture and agricultural use outside city areas is probably the main cause of subsidence along these coastal areas.
Yanan Du; Guangcai Feng; Lin Liu; Haiqiang Fu; Xing Peng; Debao Wen. Understanding Land Subsidence Along the Coastal Areas of Guangdong, China, by Analyzing Multi-Track MTInSAR Data. Remote Sensing 2020, 12, 299 .
AMA StyleYanan Du, Guangcai Feng, Lin Liu, Haiqiang Fu, Xing Peng, Debao Wen. Understanding Land Subsidence Along the Coastal Areas of Guangdong, China, by Analyzing Multi-Track MTInSAR Data. Remote Sensing. 2020; 12 (2):299.
Chicago/Turabian StyleYanan Du; Guangcai Feng; Lin Liu; Haiqiang Fu; Xing Peng; Debao Wen. 2020. "Understanding Land Subsidence Along the Coastal Areas of Guangdong, China, by Analyzing Multi-Track MTInSAR Data." Remote Sensing 12, no. 2: 299.
A number of studies have revealed a correlation between bus stops and crimes, especially street robberies. However, few have looked into the impact of bus stop location changes on the distribution of street robberies. Will newly added bus stops attract more street robberies? Will the removal of existing bus stops reduce street robberies? By assessing the change of street robberies in relation to the spatial change of bus stops of Cincinnati, OH, with the consideration of the controls from socioeconomic characteristics, point of interests (POI) and spatial heterogeneity, this study uses before-and-after comparisons and the difference-in-differences (DID) analysis in the context of quasi-experiment to answer these questions. This study assesses not only the influences of the relocation of bus stops, but also the influence on street robberies of the time elapsed from the addition or removal of bus stops. Besides the three typical variables representing the presence or absence of the intervention, before or after the intervention and the interaction of the two, we add the time from addition/removal to the DID analysis. Results suggest that, on average, adding bus stops to a new location significantly increases street robberies in the areas surrounding the stops. The longer the time from the addition of a new bus stop, the more the street robberies in its surrounding areas. Removing all bus stops from a location decreases street robberies in the areas nearby; however, this influence is not statistically significant. This suggests that the relationship between street robbery and time from removal may not be linear. There are multiple studies exploring the static relationship between the bus stop and street robbery, but none looked into their dynamic relationship. This study represents the first attempt to do so. Its findings add new evidence to the theories of rational choice, routine activity, crime pattern, and crime displacement.
Lin Liu; Minxuan Lan; John E. Eck; Emily Kang. Assessing the effects of bus stop relocation on street robbery. Computers, Environment and Urban Systems 2020, 80, 101455 .
AMA StyleLin Liu, Minxuan Lan, John E. Eck, Emily Kang. Assessing the effects of bus stop relocation on street robbery. Computers, Environment and Urban Systems. 2020; 80 ():101455.
Chicago/Turabian StyleLin Liu; Minxuan Lan; John E. Eck; Emily Kang. 2020. "Assessing the effects of bus stop relocation on street robbery." Computers, Environment and Urban Systems 80, no. : 101455.
Kernel density estimation (KDE) is widely adopted to show the overall crime distribution and at the same time obscure exact crime locations due to the confidentiality of crime data in many countries. However, the confidential level of crime locational information in the KDE map has not been systematically investigated. This study aims to examine whether a kernel density map could be reverse-transformed to its original map with discrete crime locations. Using the Epanecknikov kernel function, a default setting in ArcGIS for density mapping, the transformation from a density map to a point map was conducted with various combinations of parameters to examine its impact on the deconvolution process (density to point location). Results indicate that if the bandwidth parameter (search radius) in the original convolution process (point to density) was known, the original point map could be fully recovered by a deconvolution process. Conversely, when the parameter was unknown, the deconvolution process would be unable to restore the original point map. Experiments on four different point maps—a random point distribution, a simulated monocentric point distribution, a simulated polycentric point distribution, and a real crime location map—show consistent results. Therefore, it can be concluded that the point location of crime events cannot be restored from crime density maps as long as parameters such as the search radius parameter in the density mapping process remain confidential.
Zengli Wang; Lin Liu; Hanlin Zhou; Minxuan Lan. How Is the Confidentiality of Crime Locations Affected by Parameters in Kernel Density Estimation? ISPRS International Journal of Geo-Information 2019, 8, 544 .
AMA StyleZengli Wang, Lin Liu, Hanlin Zhou, Minxuan Lan. How Is the Confidentiality of Crime Locations Affected by Parameters in Kernel Density Estimation? ISPRS International Journal of Geo-Information. 2019; 8 (12):544.
Chicago/Turabian StyleZengli Wang; Lin Liu; Hanlin Zhou; Minxuan Lan. 2019. "How Is the Confidentiality of Crime Locations Affected by Parameters in Kernel Density Estimation?" ISPRS International Journal of Geo-Information 8, no. 12: 544.
As 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.
Minxuan Lan; Lin Liu; Andres Hernandez; Weiyi Liu; Hanlin Zhou; Zengli Wang. The Spillover Effect of Geotagged Tweets as a Measure of Ambient Population for Theft Crime. Sustainability 2019, 11, 6748 .
AMA StyleMinxuan Lan, Lin Liu, Andres Hernandez, Weiyi Liu, Hanlin Zhou, Zengli Wang. The Spillover Effect of Geotagged Tweets as a Measure of Ambient Population for Theft Crime. Sustainability. 2019; 11 (23):6748.
Chicago/Turabian StyleMinxuan Lan; Lin Liu; Andres Hernandez; Weiyi Liu; Hanlin Zhou; Zengli Wang. 2019. "The Spillover Effect of Geotagged Tweets as a Measure of Ambient Population for Theft Crime." Sustainability 11, no. 23: 6748.