The Crime Mapping application is an interactive map that allows users to query and view different types of crimes nation-wide. The application also provides the ability to create reports, charts, and print selected information.

CrimeMapTutorial is a step-by-step tutorial for learning crime mapping using ArcView GIS or MapInfo Professional GIS. It was designed to give users a thorough introduction to most of the knowledge and skills needed to produce daily maps and spatial data queries that uniformed officers and detectives find valuable for crime prevention and enforcement. The tutorials can be used either for self-learning or in a laboratory setting. The geographic information system (GIS) and police data were supplied by the Rochester, New York, Police Department. For each mapping software package, there are three PDF tutorial workbooks and one WinZip archive containing sample data and maps. Workbook 1 was designed for GIS users who want to learn how to use a crime-mapping GIS and how to generate maps and data queries. Workbook 2 was created to assist data preparers in processing police data for use in a GIS. This includes address-matching of police incidents to place them on pin maps and aggregating crime counts by areas (like car beats) to produce area or choropleth maps. Workbook 3 was designed for map makers who want to learn how to construct useful crime maps, given police data that have already been address-matched and preprocessed by data preparers. It is estimated that the three tutorials take approximately six hours to complete in total, including exercises.

The Regional Crime Analysis GIS (RCAGIS) is an Environmental Systems Research Institute (ESRI) MapObjects-based system that was developed by the United States Department of Justice Criminal Division Geographic Information Systems (GIS) Staff, in conjunction with the Baltimore County Police Department and the Regional Crime Analysis System (RCAS) group, to facilitate the analysis of crime on a regional basis. The RCAGIS system was designed specifically to assist in the analysis of crime incident data across jurisdictional boundaries. Features of the system include: (1) three modes, each designed for a specific level of analysis (simple queries, crime analysis, or reports), (2) wizard-driven (guided) incident database queries, (3) graphical tools for the creation, saving, and printing of map layout files, (4) an interface with CrimeStat spatial statistics software developed by Ned Levine and Associates for advanced analysis tools such as hot spot surfaces and ellipses, (5) tools for graphically viewing and analyzing historical crime trends in specific areas, and (6) linkage tools for drawing connections between vehicle theft and recovery locations, incident locations and suspects' homes, and between attributes in any two loaded shapefiles. RCAGIS also supports digital imagery, such as orthophotos and other raster data sources, and geographic source data in multiple projections. RCAGIS can be configured to support multiple incident database backends and varying database schemas using a field mapping utility.

As a first step in understanding law enforcement agencies' use and knowledge of crime mapping, the Crime Mapping Research Center (CMRC) of the National Institute of Justice conducted a nationwide survey to determine which agencies were using geographic information systems (GIS), how they were using them, and, among agencies that were not using GIS, the reasons for that choice. Data were gathered using a survey instrument developed by National Institute of Justice staff, reviewed by practitioners and researchers with crime mapping knowledge, and approved by the Office of Management and Budget. The survey was mailed in March 1997 to a sample of law enforcement agencies in the United States. Surveys were accepted until May 1, 1998. Questions asked of all respondents included type of agency, population of community, number of personnel, types of crimes for which the agency kept incident-based records, types of crime analyses conducted, and whether the agency performed computerized crime mapping. Those agencies that reported using computerized crime mapping were asked which staff conducted the mapping, types of training their staff received in mapping, types of software and computers used, whether the agency used a global positioning system, types of data geocoded and mapped, types of spatial analyses performed and how often, use of hot spot analyses, how mapping results were used, how maps were maintained, whether the department kept an archive of geocoded data, what external data sources were used, whether the agency collaborated with other departments, what types of Department of Justice training would benefit the agency, what problems the agency had encountered in implementing mapping, and which external sources had funded crime mapping at the agency. Departments that reported no use of computerized crime mapping were asked why that was the case, whether they used electronic crime data, what types of software they used, and what types of Department of Justice training would benefit their agencies.

Real-Time Crime Mapping Market Outlook

According to our latest research, the global Real-Time Crime Mapping market size reached USD 8.1 billion in 2024, with a robust growth driven by increasing adoption of advanced analytics and digital mapping technologies. The market is expected to expand at a CAGR of 13.4% from 2025 to 2033, reaching a projected value of USD 25.3 billion by 2033. This growth is propelled by the rising need for efficient crime prevention, rapid incident response, and the integration of AI-driven solutions in public safety infrastructure worldwide.

One of the primary growth factors for the Real-Time Crime Mapping market is the increasing demand for proactive public safety measures. Law enforcement agencies across the globe are prioritizing digital transformation to improve their crime response times and predictive policing capabilities. The integration of real-time data analytics with geographic information systems (GIS) allows agencies to visualize crime hotspots, deploy resources more efficiently, and make data-driven decisions. Furthermore, the surge in urbanization and the complexity of modern cities have necessitated more sophisticated surveillance and incident management systems, driving the adoption of real-time crime mapping solutions.

Another significant driver is the technological advancements in artificial intelligence, machine learning, and big data analytics. These technologies have revolutionized the way crime data is captured, analyzed, and presented. Real-time crime mapping platforms now offer predictive analytics, automated alerts, and seamless integration with other law enforcement databases. This has enabled agencies to not only map current incidents but also anticipate potential criminal activities and prevent them before they occur. The continuous innovation in cloud-based solutions and mobile applications further enhances accessibility and usability, making these tools indispensable for both government and commercial sectors.

The growing emphasis on community engagement and transparency has also contributed to the expansion of the Real-Time Crime Mapping market. Public-facing crime mapping portals empower citizens with timely information about incidents in their neighborhoods, fostering greater trust in law enforcement agencies. Additionally, the rise of smart city initiatives and increased investments in public safety infrastructure by governments worldwide have created a fertile environment for market growth. These initiatives often include the deployment of integrated surveillance, emergency response, and data sharing platforms, all of which rely heavily on real-time crime mapping technologies.

From a regional perspective, North America continues to dominate the Real-Time Crime Mapping market, accounting for the largest share in 2024. This is attributed to the presence of advanced law enforcement infrastructure, high adoption rates of digital technologies, and substantial government funding for public safety projects. However, the Asia Pacific region is expected to witness the fastest growth during the forecast period, driven by rapid urbanization, increasing crime rates, and growing investments in smart city and surveillance projects. Europe also remains a significant market, with countries focusing on modernizing their public safety systems and enhancing cross-border crime data sharing. Latin America and the Middle East & Africa are gradually catching up, supported by rising awareness and international collaborations in crime prevention.

Component Analysis

The component segment of the Real-Time Crime Mapping market is categorized into software, hardware, and services, each playing a pivotal role in the ecosystem. Software solutions form the backbone of real-time crime mapping systems, encompassing GIS platforms, analytics engines, and user interfaces. These solutions enable the collection, processing, and visualization of crime data in real time, allowing law enforcement agencies to generate actionable insights.

The Tempe Police Department prides itself in its continued efforts to reduce harm within the community and is providing this dataset on hate crime incidents that occur in Tempe.The Tempe Police Department documents the type of bias that motivated a hate crime according to those categories established by the FBI. These include crimes motivated by biases based on race and ethnicity, religion, sexual orientation, disability, gender and gender identity.The Bias Type categories provided in the data come from the Bias Motivation Categories as defined in the Federal Bureau of Investigation (FBI) National Incident-Based Reporting System (NIBRS) manual, version 2020.1 dated 4/15/2021. The FBI NIBRS manual can be found at https://www.fbi.gov/file-repository/ucr/ucr-2019-1-nibrs-user-manua-093020.pdf with the Bias Motivation Categories found on pages 78-79.Although data is updated monthly, there is a delay by one month to allow for data validation and submission.Information about Tempe Police Department's collection and reporting process for possible hate crimes is included in https://storymaps.arcgis.com/stories/a963e97ca3494bfc8cd66d593eebabaf.Additional InformationSource: Data are from the Law Enforcement Records Management System (RMS)Contact: Angelique BeltranContact E-Mail: angelique_beltran@tempe.govData Source Type: TabularPreparation Method: Data from the Law Enforcement Records Management System (RMS) are entered by the Tempe Police Department into a GIS mapping system, which automatically publishes to open data.Publish Frequency: MonthlyPublish Method: New data entries are automatically published to open data. Data Dictionary

Retirement Notice: This item is in mature support as of June 2023 and will be retired in December 2025. A replacement item has not been identified at this time. Esri recommends updating your maps and apps to phase out use of this item.This map shows the total crime index in the U.S. in 2022 in a multi-scale map (by state, county, ZIP Code, tract, and block group). The layer uses 2020 Census boundaries. The pop-up is configured to include the following information for each geography level:Total crime indexPersonal and Property crime indices Sub-categories of personal and property crime indices Permitted use of this data is covered in the DATA section of the EsriMaster Agreement (E204CW) and these supplemental terms.

This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Research & Development Division of the Chicago Police Department at 312.745.6071 or RandD@chicagopolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited. The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data is updated daily Tuesday through Sunday. The dataset contains more than 65,000 records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. To access a list of Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) codes, go to http://data.cityofchicago.org/Public-Safety/Chicago-Police-Department-Illinois-Uniform-Crime-R/c7ck-438e

Crime Mapping Hotspot Prediction Market Outlook

According to our latest research, the global Crime Mapping Hotspot Prediction market size reached USD 2.9 billion in 2024, with a robust year-on-year growth trajectory. The market is expected to expand at a CAGR of 18.7% from 2025 to 2033, ultimately reaching an estimated value of USD 13.1 billion by the end of the forecast period. This remarkable growth is primarily driven by the increasing adoption of advanced analytics and artificial intelligence by law enforcement agencies and urban planners worldwide, aiming to enhance public safety and optimize resource allocation.

The surge in demand for crime mapping hotspot prediction solutions is fundamentally linked to the exponential rise in data generation from surveillance systems, social media, and IoT-enabled devices. Law enforcement agencies are increasingly leveraging these technologies to proactively identify crime-prone areas, anticipate criminal activities, and allocate resources more efficiently. The integration of machine learning and artificial intelligence with geographic information systems (GIS) has significantly improved the accuracy and timeliness of crime predictions, enabling authorities to take preemptive measures and reduce crime rates. Furthermore, the proliferation of smart city initiatives across both developed and emerging economies has accelerated the deployment of these systems, as municipalities seek to enhance urban security and foster safer communities.

Another significant growth factor is the rising emphasis on data-driven decision-making in public safety management. Governments and private security firms are recognizing the value of predictive analytics in not only reducing crime but also optimizing operational costs and improving response times. The evolution of cloud computing and scalable software solutions has made advanced crime mapping tools accessible to a broader range of end-users, including smaller municipalities and private organizations. Additionally, increased funding for law enforcement modernization and digital transformation projects, especially in North America and Europe, is fueling investments in next-generation crime prediction platforms. These trends are further complemented by growing public awareness and demand for safer urban environments, pushing stakeholders to adopt innovative solutions for crime prevention.

Moreover, the ongoing advancements in artificial intelligence and machine learning algorithms are enhancing the capabilities of crime mapping hotspot prediction systems. These technologies enable the analysis of vast and complex datasets, uncovering hidden patterns and correlations that traditional methods might overlook. As a result, predictive policing strategies are becoming more effective, leading to higher arrest rates, reduced crime incidents, and improved community trust in law enforcement. The integration of real-time data feeds and mobile applications is also facilitating faster communication and collaboration among agencies, further boosting the market’s growth prospects.

Regionally, North America continues to dominate the crime mapping hotspot prediction market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The United States, in particular, has witnessed substantial investments in smart policing and public safety analytics, driven by federal and state-level initiatives. Meanwhile, the Asia Pacific region is expected to experience the fastest CAGR during the forecast period, propelled by rapid urbanization, increasing crime rates, and government focus on smart city development. Latin America and the Middle East & Africa are also emerging as promising markets, as local authorities seek to tackle rising urban crime through technological innovation and international collaboration.

Component Analysis

The component segment of the crime mapping hotspot prediction market is categorized into software, hardware, and services. Software solutions currently ho

Crime Mapping Hotspot Prediction Market Outlook

According to our latest research, the global Crime Mapping Hotspot Prediction market size reached USD 4.2 billion in 2024, with a robust CAGR of 13.7% projected through the forecast period. By 2033, the market is forecasted to attain a valuation of approximately USD 13.1 billion, driven by the increasing integration of advanced analytics and artificial intelligence in law enforcement and public safety initiatives. The primary growth factor remains the escalating need for proactive crime prevention and the adoption of predictive policing technologies by government agencies and private security organizations worldwide.

The growth trajectory of the Crime Mapping Hotspot Prediction market is largely attributed to the rapid digital transformation in the public safety sector. Law enforcement agencies globally are leveraging data-driven solutions to anticipate, identify, and respond to crime hotspots with greater efficacy. The proliferation of IoT devices, surveillance cameras, and sensor networks has resulted in a massive influx of real-time data, which, when analyzed using sophisticated machine learning and AI algorithms, enables the identification of trends and patterns that were previously undetectable. This shift towards predictive analytics is not only enhancing operational efficiency but also reducing crime rates in urban and suburban areas. The demand for such solutions is further fueled by increasing urbanization, which brings about complex security challenges that traditional policing methods struggle to address effectively.

Another significant driver for the market is the growing emphasis on community policing and public safety awareness. Municipalities and city planners are increasingly integrating Crime Mapping Hotspot Prediction tools into their urban management strategies to ensure safer environments for residents and businesses. These solutions help in resource optimization, allowing authorities to allocate patrols and law enforcement personnel more strategically. Furthermore, the integration of Geographic Information Systems (GIS) with advanced analytics platforms is enabling the visualization of crime data over time and space, supporting data-driven decision-making at all levels of governance. The market is also benefitting from rising investments in smart city projects, where public safety remains a cornerstone of sustainable urban development.

The rapid advancements in cloud computing and the growing acceptance of cloud-based deployment models are also contributing to market expansion. Cloud solutions offer scalability, cost-effectiveness, and accessibility, enabling even smaller municipalities and private security organizations to implement sophisticated crime mapping technologies without significant upfront investments in IT infrastructure. Additionally, the increasing collaboration between technology vendors, law enforcement agencies, and academic institutions is fostering innovation, leading to the development of more accurate and user-friendly prediction models. The convergence of AI, big data, and GIS technologies is expected to further revolutionize the market, making predictive policing an indispensable tool for modern law enforcement.

Regionally, North America continues to dominate the Crime Mapping Hotspot Prediction market, accounting for the largest share in 2024, primarily due to the early adoption of advanced policing technologies and substantial government funding for public safety initiatives. Europe follows closely, with significant investments in smart city infrastructure and cross-border security collaborations. The Asia Pacific region is witnessing the fastest growth, driven by rapid urbanization, rising crime rates, and increasing government focus on modernizing law enforcement agencies. Latin America and the Middle East & Africa are also emerging as promising markets, with growing awareness and gradual adoption of predictive crime mapping solutions, although challenges such as limited IT infrastructure and budget constraints persist in these regions.

Component Analysis

The Crime Mapping Hotspot Prediction market is segmented by component into software, hardware, and services, each playing a pivotal role in the overall ecosystem. The software segment holds the largest share, as it encompasses the core analytical platforms, visualization tools, and dashboard interfaces that empower users to derive actionable insights from crime data. These s

Abstract (en): This research project was designed to demonstrate the contributions that Geographic Information Systems (GIS) and spatial analysis procedures can make to the study of crime patterns in a largely nonmetropolitan region of the United States. The project examined the extent to which the relationship between various structural factors and crime varied across metropolitan and nonmetropolitan locations in Appalachia over time. To investigate the spatial patterns of crime, a georeferenced dataset was compiled at the county level for each of the 399 counties comprising the Appalachian region. The data came from numerous secondary data sources, including the Federal Bureau of Investigation's Uniform Crime Reports, the Decennial Census of the United States, the Department of Agriculture, and the Appalachian Regional Commission. Data were gathered on the demographic distribution, change, and composition of each county, as well as other socioeconomic indicators. The dependent variables were index crime rates derived from the Uniform Crime Reports, with separate variables for violent and property crimes. These data were integrated into a GIS database in order to enhance the research with respect to: (1) data integration and visualization, (2) exploratory spatial analysis, and (3) confirmatory spatial analysis and statistical modeling. Part 1 contains variables for Appalachian subregions, Beale county codes, distress codes, number of families and households, population size, racial and age composition of population, dependency ratio, population growth, number of births and deaths, net migration, education, household composition, median family income, male and female employment status, and mobility. Part 2 variables include county identifiers plus numbers of total index crimes, violent index crimes, property index crimes, homicides, rapes, robberies, assaults, burglaries, larcenies, and motor vehicle thefts annually from 1977 to 1996. The spatial dynamics of crime in nonmetropolitan locations can be understood as a product of social, economic, and demographic influences that are often unique to those areas. Thus there is a need for research on nonmetropolitan crime that takes location and geographic context seriously. This research project was designed to demonstrate the contributions that Geographic Information Systems (GIS) and spatial analysis procedures can make to the study of crime patterns in a largely nonmetropolitan region of the United States. The project examined the extent to which the relationship between various structural factors and crime varied across metropolitan and nonmetropolitan locations in Appalachia over time. GIS and crime mapping technologies enabled the researcher to look more rigorously at the spatial patterns and ecological contexts of crime. To investigate the spatial patterns of crime for this project, a georeferenced dataset was compiled at the county level for each of the 399 counties comprising the Appalachian region. The data came from numerous secondary data sources, including the Federal Bureau of Investigation's Uniform Crime Reports, the Decennial Census of the United States, the Department of Agriculture, and the Appalachian Regional Commission. Data were gathered on the demographic distribution, change, and composition of each county, as well as other socioeconomic indicators. The dependent variables were index crime rates derived from the Uniform Crime Reports, with separate variables for violent and property crimes. These data were integrated into a GIS database in order to enhance the research with respect to: (1) data integration and visualization, (2) exploratory spatial analysis, and (3) confirmatory spatial analysis and statistical modeling. In order to portray the contextual diversity of crime in Appalachia, three different county classifications, each based on different criteria, were employed: (1) Appalachian subregions, consisting of North, Central, and South Appalachia, (2) Beale county codes based on metro-nonmetro designations, population size, and adjacency to metropolitan counties, and (3) distressed county codes based on measures of poverty, unemployment, and per capita income. Part 1 contains variables for Appalachian subregions, Beale county codes, distress codes, number of families and households, population size, racial and age composition of population, dependency ratio, population growth, number of births and deaths, net migration, education, household ...

The City of Ferndale uses the service CrimeMapping.com to provide near-live mapping of local crimes, sorted by category. Our goal in providing this information is to reduce crime through a better-informed citizenry. Crime reports older than 180 days can be accessed in this data set. For near-live crime data, go to crimemapping.com. this is a subset of this historic data that has been geocoded to allow for easy analysis and mapping in a different data set. It contains all easily geocoded addresses. A complete CSV file covering all crime reports from 5/2011 to 5/2017 is also available.

This research study analysed the crime rate spatially and it examined the relationship between crime and spatial factors in Saudi Arabia. It reviewed the related literature that has utilised crime mapping techniques, such as Geographic Information Systems (GIS) and remote sensing (RS); these techniques are a basic part of effectively helping security and authority agencies by providing them with a clear perception of crime patterns and a surveillance direction to track and tackle crime. This study analysed the spatial relationships between crime and place, immigration, changes in urban areas, weather and transportation networks. The research study was divided into six parts to investigate the correlation between crime and these factors. The first part of the research study examined the relationship between crime and place across the 13 provinces of Saudi Arabia using GIS techniques based on population density in order to identify and visualise the spatial distributions of national and regional crime rates for drug crimes, thefts, murders, assaults, and alcohol-related and ‘outrageous crimes’ (offences against Islam) over a 10-year period from 2003 to 2012. Social disorganisation theory was employed to guide the study and explain the diversity in crime patterns across the country. The highest rates of overall crimes were identified in the Northern Borders Province and Jizan, which are located in the northern and southern regions of the country, respectively; the eastern area of the country was found to have the lowest crime rate. Most drug offences occurred in the Northern Borders Province and Jizan; high rates of theft were recorded in the Northern Borders Province, Jouf Province and Makkah Province, while the highest rates of homicide occurred in Asir Province. The second part of the research study aimed to determine the trends of overall crime in relation to six crime categories: drug-related activity, theft, murder, assault, alcohol-related crimes and outrageous or sex-related crimes, in Saudi Arabia’s 13 provinces over a 10-year period from 2003 to 2012. The study analysed the spatial and temporal changes of criminal cases. Spatial changes were used to determine the differences over the time period of 2003–2012 to show the provincial rates of change for each crime category. Temporal changes were used to compute the trends of the overall crime rate and crimes in the six categories per 1,000 people per year. The results showed that the overall crime rate increased steadily until 2008; thereafter it decreased in all areas except for the Northern Borders Province and Jizan, which recorded the highest crime rates throughout the study period. We have explained that decrease in terms of changes in wages, support for the unemployed and service improvements, which were factors that previous studies also emphasised as being the primary cause for the decrease. This study includes a detailed discussion to contribute to the understanding of the changes in the crime rates in these categories throughout this period in the 13 provinces of Saudi Arabia. The third part of the research study aimed to explain the effects of immigration on the overall crime rate in the six most significant categories of crime in Saudi Arabia, which are drug-related activity, theft, murder, assault, alcohol-related crimes and outrageous crimes, during a 10-year period from 2003 to 2012, in all 13 administrative provinces. It also sought to identify the provinces most affected by the criminal activities of immigrants during this period. No positive association between immigrants and criminal cases was found. It was clearly visible that the highest rate of overall criminal activities was in the south, north and Makkah areas, where there is a high probability of illegal immigrants. This finding supports the basic criminological theory that areas with high levels of immigrants also experience high rates of crime. The study’s results provide recommendations to the Saudi government, policy-makers, decision-makers and immigration authorities, which could assist in reducing crimes perpetrated by immigrants. In the fourth part of the research study, urban areas were examined in relation to crime rates. Urban area expansion is one of the most critical types of worldwide change, and most urban areas are experiencing increased population growth and infrastructure development. Urban change leads to many changes in the daily activities of people living within an affected area. Many studies have suggested that urbanisation and crime are related. However, those studies focused on land uses, types of land use and urban forms, such as the physical features of neighbourhoods, roads, shopping centres and bus stations. It is very important for criminologists and urban planning decision-makers to understand the correlation between urban area expansion and crime. In this research, satellite images were used to measure urban expansion over a 10-year period; the study tested the correlations between these expansions and the number of criminal activities within these specific areas. The results show that there is a measurable relationship between urban expansion and criminal activities. The findings support the crime opportunity theory as one possibility, which suggests that population density and crime are conceptually related. Moreover, the results show that the correlations are stronger in areas that have undergone greater urban growth. This study did not evaluate many other factors that might affect the crime rate, such as information on the spatial details of the population, city planning, economic considerations, the distance from the city centre, the quality of neighbourhoods, and the number of police officers. However, this research will be of particular interest to those who aim to use remote sensing to study crime patterns. The fifth part of the research study investigated the impacts of weather on crime rates in two different cities: Riyadh and Makkah. While a number of studies have examined climate influences on crime and human behaviour by investigating the correlation between climate and weather elements, such as temperature, humidity and precipitation, and crime rates, few studies have focused on haze as a weather element and its correlation with crime. This research examined haze as a weather variable to investigate its effects on criminal activity and compare its effects with those of temperature and humidity. Monthly crime data and monthly weather records were used to build a regression model to predict crime cases based on three weather factors using temperature, humidity and haze values. This model was applied to two provinces in Saudi Arabia with different types of climates: Riyadh and Makkah. Riyadh Province is a desert area in which haze occurs approximately 17 days per month on average. Makkah Province is a coastal area where it is hazy an average of 4 days per month. A measurable relationship was found between each of these three variables and criminal activity. However, haze had a greater effect on theft, drug-related crimes and assault in Riyadh Province than temperature and humidity. Temperature and humidity were the efficacious variables in Makkah Province, while haze had no significant influence in that region. Finally, the sixth part of the research study examined the influence of the quality and extent of road networks on crime rates in both urban and rural areas in Jizan Province, Saudi Arabia. We performed both Ordinary Least Squares regression (OLS) and Geographically Weighted Regression (GWR) where crime rate was the dependent variable and paved (sealed) roads, non-paved (unsealed/gravel) roads and population density were the explanatory variables. Population density was a control variable. The findings reveal that, across all 14 districts in that province, the districts with better quality paved road networks had lower rates of crime than the districts with unpaved roads. Furthermore, the more extensive the road networks, the lower the crime rate whether or not the roads were paved. These findings concur with those reported in studies conducted in other countries, which revealed that rural areas are not always the safe, crime-free places they are often believed to be. This research contributes knowledge about the geographical information of criminal movement, and it offers some conceivable reasons for crime rates and patterns in relation to the spatial factors and the socio-cultural perspectives of Saudi Arabian life. More geographical research is still needed in terms of criminology, which will provide a better understanding of crime patterns, particularly in Saudi Arabia, and across the globe, where the spatial distribution of criminal cases is an essential base in crime research. Furthermore, additional studies are needed to investigate the complex interventions of the effect of different spatial variables on crime and the uncertainties correlation with the impact of environmental factors. This can help predict the impact of socioeconomic and environmental factors. The greater part of such an investigation will enhance the understanding of crime patterns, which is imperative for advancing a framework that can be used to address crime reduction and crime prevention.

GIS for Crime Analysis lesson - curriculum connections.

Crime data assembled by census block group for the MSA from the Applied Geographic Solutions' (AGS) 1999 and 2005 'CrimeRisk' databases distributed by the Tetrad Computer Applications Inc. CrimeRisk is the result of an extensive analysis of FBI crime statistics. Based on detailed modeling of the relationships between crime and demographics, CrimeRisk provides an accurate view of the relative risk of specific crime types at the block group level. Data from 1990 - 1996,1999, and 2004-2005 were used to compute the attributes, please refer to the 'Supplemental Information' section of the metadata for more details. Attributes are available for two categories of crimes, personal crimes and property crimes, along with total and personal crime indices. Attributes for personal crimes include murder, rape, robbery, and assault. Attributes for property crimes include burglary, larceny, and mother vehicle theft. 12 block groups have no attribute information. CrimeRisk is a block group and higher level geographic database consisting of a series of standardized indexes for a range of serious crimes against both persons and property. It is derived from an extensive analysis of several years of crime reports from the vast majority of law enforcement jurisdictions nationwide. The crimes included in the database are the "Part I" crimes and include murder, rape, robbery, assault, burglary, theft, and motor vehicle theft. These categories are the primary reporting categories used by the FBI in its Uniform Crime Report (UCR), with the exception of Arson, for which data is very inconsistently reported at the jurisdictional level. Part II crimes are not reported in the detail databases and are generally available only for selected areas or at high levels of geography. In accordance with the reporting procedures using in the UCR reports, aggregate indexes have been prepared for personal and property crimes separately, as well as a total index. While this provides a useful measure of the relative "overall" crime rate in an area, it must be recognized that these are unweighted indexes, in that a murder is weighted no more heavily than a purse snatching in the computation. For this reason, caution is advised when using any of the aggregate index values. The block group boundaries used in the dataset come from TeleAtlas's (formerly GDT) Dynamap data, and are consistent with all other block group boundaries in the BES geodatabase.

   This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase.


   The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive.


   The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders.


   Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.


   This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase.


   The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive.


   The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders.


   Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

Police station locations in Chicago

Descriptive statistics for the non-standardised and standardised dependent and independent variables used as proxies for social disorganisation characteristics in Khayelitsha and Fort Lauderdale. The statistics are presented as raw variables prior to transformations. The spatial statistical techniques used to examine spatial patterns of violent crime and the associations with social disorganisation in Khayelitsha include: - exploratory spatial data analysis (ESDA) to explore the spatial distribution of violent crime in Khayelitsha; - bivariate correlation analysis using Pearson product-moment correlation; - a series of spatial regression models to examine the association between crime and a selection of structural neighbourhood characteristics in Khayelitsha.

⚠️Due to the City's transition to a new Record Management System (RMS) , the Crime Incidents data, dashboards and maps on this site currently include records only through 11/18/25. Regular updates are paused to align and unify old and new data models. We aim to restore fully automated daily updates by the end of 2025.This transition does not impact Public Safety systems or operations. Data for the pause period (from 11/18/25 until integration completion) is still available upon request via the City’s public records portal. Thank you for your patience!This mapping application visualizes crime incidents across the City of Cleveland. The data includes details on the type, location, and time of each reported crime, enabling users to analyze crime patterns and trends. The data provided is the latest available information and is updated regularly as statistics change. For access to comprehensive reports, kindly submit a public record request by clicking here. This application uses the following dataset(s):Cleveland Crime Incidents Update FrequencyDaily around 8AM EST ContactsCity of Cleveland Division of Police InstructionsBy default, the map loads with the previous 90 days incidents.Use the filters on the left side of the page to filter the data by date, City of Cleveland Ward, Cleveland Division of Police District, and/or crime type.Use the “Reset Filters” buttons under each filter to reset each filter individually.Use the ”Query" button in the lower right to select points currently on the map by drawing shapes and defining specific locations.Use the “Map Layers” button in the lower right to turn on and off the crime incidents layer, the Cleveland Division of Police Districts, and/or the City of Cleveland Wards.Use the “Tables” to view the data tables for the incidents that are map or queried.Click a point on the map to pop up individual information about the point(s) that were selected.Data from the map can be exported to CSV, JSON, and GeoJSON files directly from the tables which are accessed through the “Tables” button in the lower right corner of the map.

The Sheriff's Office provides an online mapping and analysis service that combines the value of law enforcement data with the ease of use of Google-based mapping and an analytics module so that members of the public can view police data in a high-impact map or summary descriptive format.

The online mapping tool allows residents to view information about crimes relevant to their community.

View daily crime reports and significant incident reports.

This collection grew out of a prototype case tracking and crime mapping application that was developed for the United States Attorney's Office (USAO), Southern District of New York (SDNY). The purpose of creating the application was to move from the traditionally episodic way of handling cases to a comprehensive and strategic method of collecting case information and linking it to specific geographic locations, and collecting information either not handled at all or not handled with sufficient enough detail by SDNY's existing case management system. The result was an end-user application designed to be run largely by SDNY's nontechnical staff. It consisted of two components, a database to capture case tracking information and a mapping component to link case and geographic data. The case tracking data were contained in a Microsoft Access database and the client application contained all of the forms, queries, reports, macros, table links, and code necessary to enter, navigate through, and query the data. The mapping application was developed using Environmental Systems Research Institute's (ESRI) ArcView 3.0a GIS. This collection shows how the user-interface of the database and the mapping component were customized to allow the staff to perform spatial queries without having to be geographic information systems (GIS) experts. Part 1 of this collection contains the Visual Basic script used to customize the user-interface of the Microsoft Access database. Part 2 contains the Avenue script used to customize ArcView to link the data maintained in the server databases, to automate the office's most common queries, and to run simple analyses.