Wildlife crimes that involve smuggling threaten national security and biodiversity, cause regional conflicts, and hinder economic development, especially in developing countries with abundant wildlife resources. Over the past few decades, significant headway has been made in combating wildlife smuggling and the related illegal domestic trade in China. Previous studies on the wildlife smuggling trade were mostly based on customs punishment and confiscation data. From the China Judgments Online website, we retrieved cases related to cross-border wildlife and wildlife products smuggling from 2014 to 2020. A total of 510 available cases and 927 records for more than 110 species were registered. We thoroughly studied each judgment and ruling file to extract information on cases, defendants, species, sentences, and origins and destinations of wildlife and wildlife products. Furthermore, frequency of origin-destination place occurrences and spatial patterns of cross-border wildlife crime in China were shown in this data paper. The main purpose of our dataset is to make these wildlife and wildlife products trade data accessible for researchers to develop conservation studies. We expect that this dataset will be valuable for network analysis of regional or global wildlife trafficking, which has attracted global attention. There are no copyright restrictions on the data; we ask that researchers please cite this paper and the associated dataset when using the data in publications. Methods Data source: The China Judgments Online (CJO) website (https://wenshu.court.gov.cn) provides electronic public access to court records. In 2010, 2013, and 2016, the Supreme People’s Court promulgated and revised the provisions on the publication of judicial documents by people’s courts on the Internet, and the publication of judicial documents has become the responsibility and obligation of courts at all levels (Wu, 2022). Since January 1, 2014, judgment documents must be published on CJO within seven days of their enforcement, and cannot be amended, replaced or revoked without court authority. Up to now, the CJO has become an important channel for the publication of judgments documents.

Data collection: The collection time of this dataset is up to September 2021. We searched for “wildlife” and “smuggling” on the China referee’s website. Then, we screened these judgment documents according to the following criteria: (I) the full text can be accessed, and the case involves the crimes of illegal hunting, sale, acquisition, transportation, or smuggling of wildlife or wildlife products (including rare and endangered wildlife or wildlife products) overseas and (II) when there are multiple judgment documents in the same lawsuit, such as any subsequent retrial of a case, filing and hearing of different perpetrators in batches, a consistent case number (record) was assigned.

Data compilation: These judicial documents provide the process of tracing criminal information. We collected as detailed information as possible, such as the date of the seizure, the location of the seizure, the type of illegal activities, the items seized, the source of the items seized, and the actual or expected destination. We used these criteria: (I) on the premise of protecting the personal information in the judgment documents, we obtained the education level and nationality of the principal defendants; (II) for the origin and destination of wildlife or its products, in addition to recording the national, provincial, county, and city levels, the information should be as accurate as possible to specific geographical names by obtaining longitude and latitude coordinate data through Baidu map (https://map.baidu.com/) and Google map (https://www.google.com/maps); and (III) for the identification of “crocodile,” “modern elephant,” “pangolin scale,” and other identifications that are not accurate to the species level in the judgment documents, only the upper classification (genus) level was recorded (i.e., “Crocodylus,” “Loxodonta,” “Manis”; Figure 3). If only the Chinese common name of the species was given but the Latin scientific name was not given, we queried the corresponding species in the International Union for the Conservation of Nature (IUCN)’s Red List of Threatened Species (hereafter: IUCN Red List; https://www.iucnredlist.org) for supplemental information. Eventually these records were translated from Chinese to English.

Quality control: Due to the need to extract information by reading many parties’ statements, defenders’ opinions, examination instructions, and other words, the preliminary preparation was mainly to discuss the standardized methods and steps of data collection, and the division of labor and training of personnel involved in data collection tasks. In the data entry and summary stage, established data collection methods and steps were followed to reduce human errors. In the data inspection stage, we cross checked the obtained data and missing values with the author to ensure the accuracy of data input. If there were questions, the lead author and Luo would revisit the original judgment documents and make a final decision after discussion with the other authors.

As a part of the effort to promote e-governance and judicial transparency, China has been promoting mass online digitalization of court, including an archive of judgment text (司法文书), a platform of online trial videos and live broadcasting (庭审直播), and a judgment implementation tracker (判决执行). China has become one of the few countries that allow cameras in the courtroom. Though a growing number of studies use court decision data, little research has been conducted on the court trial videos. The goal of the Chinese Courtroom Video Database is to meet the needs of those interested in broad research of government policy diffusion, judicial transparency, and judicial behavior in the China context by filling the vacancy in judicial data and providing a new perspective to the existing scholarship. The database includes two sets of data. The first dataset is a catalog of half-million entries of criminal and administrative trial videos in all 31 provinces from January 2013 to February 2019. Each profile records the basic information of a trial video, such as case identification number, date and time of the trial, participants, reason of trial, location of the court, number of views of the video, and other descriptions. The second dataset is a collection of 1,491 audio files of online criminal trials in Yunnan, China. Each audio was downloaded and converted from the original video. The datasets were collected using the Selenium package of Python and Downie, an online stream downloader.

China crime rate per 100K population for 2020 was 0.50, a 3.22% decline from 2019.

<ul style='margin-top:20px;'>

<li>China crime rate per 100K population for 2019 was <strong>0.52</strong>, a <strong>2.27% decline</strong> from 2018.</li>
<li>China crime rate per 100K population for 2018 was <strong>0.53</strong>, a <strong>6.27% decline</strong> from 2017.</li>
<li>China crime rate per 100K population for 2017 was <strong>0.57</strong>, a <strong>8.01% decline</strong> from 2016.</li>
</ul>Intentional homicides are estimates of unlawful homicides purposely inflicted as a result of domestic disputes, interpersonal violence, violent conflicts over land resources, intergang violence over turf or control, and predatory violence and killing by armed groups. Intentional homicide does not include all intentional killing; the difference is usually in the organization of the killing. Individuals or small groups usually commit homicide, whereas killing in armed conflict is usually committed by fairly cohesive groups of up to several hundred members and is thus usually excluded.

Abstract (en): This study was designed by American criminologist Marvin Wolfgang as a replication of his DELINQUENCY IN A BIRTH COHORT studies conducted in Philadelphia, Pennsylvania (ICPSR 7729 and ICPSR 9293). The focus of the study is a cohort of all persons born in 1973 in the Wuchang District of the city of Wuhan. This district was selected because it was a populous commercial and residential area. The cohort birth year was chosen to reflect the impact of major economic and social changes in China. Data include interviews with all known criminal offenders as of 1990 and with a matched comparison sample. Additional residential, demographic, and updated criminal history data as of 2000 were collected on all persons born in the 1973 Wuchang District cohort. This study was designed by American criminologist Marvin Wolfgang as a replication of his DELINQUENCY IN A BIRTH COHORT studies conducted in Philadelphia, Pennsylvania (ICPSR 7729 and ICPSR 9293). It was initially a collaboration among the Chinese Society of Juvenile Delinquency Research (CSJDR), the International Exchange Association of the Ministry of Education, the Public Security Institute of the Ministry of Public Security, the Public Security Department of Hubei Province, the Public Security Bureau of Wuhan City, and the Sellin Center for Studies in Criminology and Criminal Law, Wharton School, University of Pennsylvania. The study began in 1990 with a small amount of funding from Wolfgang, but it was not completed at that time. After Wolfgang's death in 1998, the CSJDR invited the principal investigators of this study to complete the data collection and analysis of the Wuhan cohort. The goals of this project were to locate and determine what data had been collected and what data needed to be collected to complete the study, to gather the necessary data to draw conclusions regarding the accuracy of the original report of a delinquency rate of less than 2 percent, and to analyze the entire cohort dataset. The project was considered important since it was the first contribution from a non-western society to the international literature on longitudinal and cohort research. The research site, the city of Wuhan, and the birth cohort were selected by the Chinese team and Wolfgang in 1990. Wuhan is the capital city of Hubei province and one of the most important industrial cities in central China located along the Yangtze River and Hanjaing River. It is an urban, heavily industrial city with three distinct districts geographically divided by the two rivers. The cohort consists of all persons who were born in 1973 in the Wuchang District of the city of Wuhan. Wuchang was the most populous major commercial and residential area of Wuhan. It was an area that seemed most likely to experience the impact of the economic changes in China. The district was also selected as the site because of the personal contacts of the Chinese team with the authorities, which meant that access to all data and information was assured. The cohort birth year is significant because it was the first year after China's major new open policy. As such, the persons in the sample were the first to experience the impact of major economic and social changes. The original cohort, as defined by Wolfgang, consisted of 5,341 individuals. Part 1, the Offender and Matched Comparison Data, consists of 81 persons from Wolfgang's original cohort who were identified as having a criminal record, plus a matched control group of an additional 81 people. The respondents in this dataset were interviewed by Wolfgang. Data from the original instruments were never computer coded or statistically analyzed independently of the Police Statistical Bureau's summaries. Interviewers traveled in Wuhan and seven other provinces to follow-up the original subjects during 1994 and 1995, but the data they collected were never coded and analyzed because the team was dismissed due to lack of funding. Data collection for this study included locating the original hand-written data and interviews from the offender and matched comparison data collected by Wolfgang. These surveys were translated and the data were entered into an electronic database. The detailed information from the residential registration cards providing data on crime and delinquency among the original 5,341 people in the cohort were not collected in the original study and thus no information was available on the entire cohort. The only available data were from the o...

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

This handbook is meant to help immigrants and refugees who are the victims of crime, as well as those who seek to assist them. Because of language and cultural barriers, immigrant and refugee victims may not be aware of programs and may face additional challenges when involved in the criminal justice system. This handbook describes programs, services and opportunities to assist victims of crime.