These data are part of NACJD's Fast Track Release and are distributed as they there received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except of the removal of direct identifiers. Users should refer to the accompany readme file for a brief description of the files available with this collections and consult the investigator(s) if further information is needed. This study was a systematic assessment of the impacts of foreclosures and crime levels on each other, using sophisticated spatial analysis methods, informed by qualitative research on the topic. Using data on foreclosures and crime in District of Columbia and Miami-Dade County, Florida from 2003 to 2011, this study considered the effects of the two phenomena on each other through a dynamic systems approach.

The purpose of this study was to assess both the school-level effects and the participant-level effects of Youth Crime Watch (YCW) programs. Abt Associates conducted a four-year impact evaluation of Youth Crime Watch (YCW) programs in three Florida school districts (Broward, Hillsborough, and Pinellas Counties). School-based YCW programs implement one or more of a variety of crime prevention activities, including youth patrol, in which YCW participants patrol their school campus and report misconduct and crime. The evaluation collected both School-Level Data (Part 1) and Student-Level Data (Part 2). The School-Level Data (Part 1) contain 9 years of data on 172 schools in the Broward, Hillsborough, and Pinellas school districts, beginning in the 1997-1998 school year and continuing through the 2005-2006 school year. A total of 103 middle schools and 69 high schools were included, yielding a total of 1,548 observations. These data provide panel data on reported incidents of crime and violence, major disciplinary actions, and school climate data across schools and over time. The Student-Level Data (Part 2) were collected between 2004 and 2007 and are comprised of two major components: (1) self-reported youth attitude and school activities survey data that were administered to a sample of students in middle schools in the Broward, Hillsborough, and Pinellas School Districts as part of a participant impact analysis, and (2) self-reported youth attitude and school activities survey data that were administered to a sample of YCW continuing middle school students and YCW high school students in the same three school districts as part of a process analysis. For Part 2, a total of 3,386 completed surveys were collected by the project staff including 1,319 "new YCW" student surveys, 1,581 "non-YCW" student surveys, and 486 "Pro" or "Process" student surveys. The 138 variables in the School-Level Data (Part 1) include Youth Crime Watch (YCW) program data, measures of crime and the level of school safety in a school, and other school characteristics. The 99 variables in the Student-Level Data (Part 2) include two groups of questions for assessing participant impact: (1) how the respondents felt about themselves, and (2) whether the respondent would report certain types of problems or crimes that they observed at the school. Part 2 also includes administrative variables and demographic/background information. Other variables in Part 2 pertain to the respondent's involvement in school-based extracurricular activities, involvement in community activities, attitudes toward school, attitudes about home environment, future education plans, attitudes toward the YCW advisor, attitudes about effects of YCW, participation in YCW, reasons for joining YCW, and reasons for remaining in YCW.

Disclaimer: Crime Responses is provided by the Gainesville Police Department (GPD) to document initial details surrounding an incident to which GPD officers respond. This dataset contains crime incidents from 2011 to present and includes a reduced set of fields focused on capturing the type of incident as well when and where an incident occurred. The Incident location addresses have been rounded off and are not the exact location due to the constitutional amendment known as "Marsy's Law".

In 2021, Florida reporting of crime data began a transition from Summary Reporting System (SRS) to National Incident-Based Reporting System (NIBRS), causing an effect on crime statistics reported by Law Enforcement Agencies such as the Gainesville Police Department who made this transition on November 16, 2021. The effect would be an increase in crime due to the elimination of the SRS Hierarchy Rule which collected only the most serious offense in an incident while NIBRS will now capture up to 10 offenses per incident and specifies more offense categories than SRS. The inclusion of these crimes, particularly property crimes, will reflect an increase in crime when switching from SRS reporting to NIBRS' reporting. The apparent increase (usually not greater than 2.7%) is simply due to the difference between how crimes are counted in NIBRS versus the SRS and its application of the Hierarchy Rule. More information regarding NIBRS effect on crime statistics can be found on the following link: https://ucr.fbi.gov/nibrs/2014/resource-pages/effects_of_nibrs_on_crime_statistics_final.pdf.

This dataset replicates the citizen reporting component of POLICE RESPONSE TIME ANALYSIS, 1975 (ICPSR 7760). Information is included on 4,095 reported incidents of aggravated assault, auto theft, burglary, larceny/theft offenses, forcible rape, and robbery. The data cover citizen calls to police between April 21 and December 7, 1979. There are four files in this collection, one each for Jacksonville, Florida, Peoria, Illinois, Rochester, New York, and San Diego, California. The data are taken from police dispatch records and police interviews of citizens who requested police assistance. Variables taken from the dispatch records include the dispatch time, call priority, police travel time, age, sex, and race of the caller, response code, number of suspects, and area of the city in which the call originated. Variables taken from the citizen interviews include respondent's role in the incident (victim, caller, victim-caller, witness-caller), incident location, relationship of caller to victim, number of victims, identification of suspect, and interaction with police.

Does the rate of violent victimization differ across race and ethnic groups? In an effort to answer this question, this study sought to examine the violent victimization rate and the factors influencing ethnic-specific rates of violence in the city of Miami. Administrative data were obtained from the United States Bureau of the Census and the Miami Police Department Research Unit. For the groups of people identified as Afro Americans, Latinos, and Haitians, the numbers who were victims of aggravated assault and robbery in 1997 are included along with the assault and robbery rates for each group. The remaining variables are the percent of female-headed households, percent below poverty line, percent of young males out of the labor force and unemployed, residential instability, vacant and household instability, and the percent of 1980-1990 immigrants.

This dataset reflects arrests in the City of Gainesville since 2011. Arrest data is provided by the Gainesville Police Department (GPD) and derived from Police reports.

Disclaimer: Crime Responses is provided by the Gainesville Police Department (GPD) to document initial details surrounding an incident to which GPD officers respond. This dataset contains crime incidents from 2011 to present and includes a reduced set of fields focused on capturing the type of incident as well when and where an incident occurred. The Incident location addresses have been rounded off and are not the exact location due to the constitutional amendment known as "Marsy's Law".

In 2021, Florida reporting of crime data began a transition from Summary Reporting System (SRS) to National Incident-Based Reporting System (NIBRS), causing an effect on crime statistics reported by Law Enforcement Agencies such as the Gainesville Police Department who made this transition on November 16, 2021. The effect would be an increase in crime due to the elimination of the SRS Hierarchy Rule which collected only the most serious offense in an incident while NIBRS will now capture up to 10 offenses per incident and specifies more offense categories than SRS. The inclusion of these crimes, particularly property crimes, will reflect an increase in crime when switching from SRS reporting to NIBRS' reporting. The apparent increase (usually not greater than 2.7%) is simply due to the difference between how crimes are counted in NIBRS versus the SRS and its application of the Hierarchy Rule. More information regarding NIBRS effect on crime statistics can be found on the following link: https://ucr.fbi.gov/nibrs/2014/resource-pages/effects_of_nibrs_on_crime_statistics_final.pdf.

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 study was an evaluation of the Breaking the Cycle (BTC) demonstration projects conducted in Birmingham, Alabama, Jacksonville, Florida, and Tacoma, Washington, between 1997 and 2001. The BTC demonstrations tested the feasibility and impact of systemwide interventions to reduce drug use among offenders by identifying and intervening with drug-involved felony defendants. This study contains data collected as part of the impact evaluation of BTC, which was designed to test the hypotheses that BTC reduced criminal involvement, substance abuse, and problems related to the health, mental health, employment, and families of felony drug defendants in the demonstration sites. The evaluation examined the relationship between changes in these areas and characteristics of the participants, the kinds and levels of services and supervision they received, and perceptions of defendants about the justice system's handling of their cases. It also assessed how BTC affected case handling and the length of time required to reach a disposition, the number of hearings, and the kinds of sentences imposed. The impact evaluation was based on a quasi-experimental comparison of defendants in BTC with samples of similar defendants arrested in the year before BTC implementation. Interviews were conducted with sample members and additional data were gathered from administrative records sources, such as the BTC programs, arrest records, and court records.

This Data includes all officer-involved shootings since 2009. Transparency is a priority for the Orlando Police Department and we will continue to include more information in this Dataset.

Starting in 2014 all officer-involved shooting investigations are reviewed by the State Attorney's Office. A link to the State Attorney's review letter is provided for investigations that are complete. Prior to 2014 only cases where the someone was hit as a result of the shooting were reviewed by the State Attorney's Office. All other cases were reviewed internally. A link to those findings are provided.

This dataset shows information about each traffic crash since 2011 on city streets within the City of Gainesville limits and under the jurisdiction of Gainesville Police Department (GPD). Data shown are more than 60 days from when accidents occurred due to Florida Statutes 316.066.

Disclaimer: In 2021, Florida reporting of crime data began a transition from Summary Reporting System (SRS) to National Incident-Based Reporting System (NIBRS), causing an effect on crime statistics reported by Law Enforcement Agencies such as the Gainesville Police Department who made this transition on November 16, 2021. The effect would be an increase in crime due to the elimination of the SRS Hierarchy Rule which collected only the most serious offense in an incident while NIBRS will now capture up to 10 offenses per incident and specifies more offense categories than SRS. The inclusion of these crimes, particularly property crimes, will reflect an increase in crime when switching from SRS reporting to NIBRS' reporting. The apparent increase (usually not greater than 2.7%) is simply due to the difference between how crimes are counted in NIBRS versus the SRS and its application of the Hierarchy Rule. More information regarding NIBRS effect on crime statistics can be found on the following link: https://ucr.fbi.gov/nibrs/2014/resource-pages/effects_of_nibrs_on_crime_statistics_final.pdf.