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.

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.

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.

CrimeStat III is a spatial statistics program for the analysis of crime incident locations, developed by Ned Levine and Associates under the direction of Ned Levine, PhD, that was funded by grants from the National Institute of Justice (grants 1997-IJ-CX-0040, 1999-IJ-CX-0044, 2002-IJ-CX-0007, and 2005-IJ-CX-K037). The program is Windows-based and interfaces with most desktop GIS programs. The purpose is to provide supplemental statistical tools to aid law enforcement agencies and criminal justice researchers in their crime mapping efforts. CrimeStat is being used by many police departments around the country as well as by criminal justice and other researchers. The program inputs incident locations (e.g., robbery locations) in 'dbf', 'shp', ASCII or ODBC-compliant formats using either spherical or projected coordinates. It calculates various spatial statistics and writes graphical objects to ArcGIS, MapInfo, Surfer for Windows, and other GIS packages. CrimeStat is organized into five sections: Data Setup Primary file - this is a file of incident or point locations with X and Y coordinates. The coordinate system can be either spherical (lat/lon) or projected. Intensity and weight values are allowed. Each incident can have an associated time value. Secondary file - this is an associated file of incident or point locations with X and Y coordinates. The coordinate system has to be the same as the primary file. Intensity and weight values are allowed. The secondary file is used for comparison with the primary file in the risk-adjusted nearest neighbor clustering routine and the duel kernel interpolation. Reference file - this is a grid file that overlays the study area. Normally, it is a regular grid though irregular ones can be imported. CrimeStat can generate the grid if given the X and Y coordinates for the lower-left and upper-right corners. Measurement parameters - This page identifies the type of distance measurement (direct, indirect or network) to be used and specifies parameters for the area of the study region and the length of the street network. CrimeStat III has the ability to utilize a network for linking points. Each segment can be weighted by travel time, travel speed, travel cost or simple distance. This allows the interaction between points to be estimated more realistically. Spatial Description Spatial distribution - statistics for describing the spatial distribution of incidents, such as the mean center, center of minimum distance, standard deviational ellipse, the convex hull, or directional mean. Spatial autocorrelation - statistics for describing the amount of spatial autocorrelation between zones, including general spatial autocorrelation indices - Moran's I , Geary's C, and the Getis-Ord General G, and correlograms that calculate spatial autocorrelation for different distance separations - the Moran, Geary, Getis-Ord correlograms. Several of these routines can simulate confidence intervals with a Monte Carlo simulation. Distance analysis I - statistics for describing properties of distances between incidents including nearest neighbor analysis, linear nearest neighbor analysis, and Ripley's K statistic. There is also a routine that assigns the primary points to the secondary points, either on the basis of nearest neighbor or point-in-polygon, and then sums the results by the secondary point values. Distance analysis II - calculates matrices representing the distance between points for the primary file, for the distance between the primary and secondary points, and for the distance between either the primary or secondary file and the grid. 'Hot spot' analysis I - routines for conducting 'hot spot' analysis including the mode, the fuzzy mode, hierarchical nearest neighbor clustering, and risk-adjusted nearest neighbor hierarchical clustering. The hierarchical nearest neighbor hot spots can be output as ellipses or convex hulls. 'Hot spot' analysis II - more routines for conducting hot spot analysis including the Spatial and Temporal Analysis of Crime (STAC), K-means clustering, Anselin's local Moran, and the Getis-Ord local G statistics. The STAC and K-means hot spots can be output as ellipses or convex hulls. All of these routines can simulate confidence intervals with a Monte Carlo simulation. Spatial Modeling Interpolation I - a single-variable kernel density estimation routine for producin

The dataset contains a subset of locations and attributes of incidents reported in the ASAP (Analytical Services Application) crime report database by the District of Columbia Metropolitan Police Department (MPD). Visit crimecards.dc.gov for more information. This data is shared via an automated process where addresses are geocoded to the District's Master Address Repository and assigned to the appropriate street block. Block locations for some crime points could not be automatically assigned resulting in 0,0 for x,y coordinates. These can be interactively assigned using the MAR Geocoder.On February 1 2020, the methodology of geography assignments of crime data was modified to increase accuracy. From January 1 2020 going forward, all crime data will have Ward, ANC, SMD, BID, Neighborhood Cluster, Voting Precinct, Block Group and Census Tract values calculated prior to, rather than after, anonymization to the block level. This change impacts approximately one percent of Ward assignments.

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 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.

A map used in the Public Crime Map application.

Check out the Crime Maps and Stats Application, an online application that displays summary statistics and enables mapping of recent incidents within a radius of an address. Also see this Crime Incidents Visualization.View metadata for key information about this dataset.Part I crimes include violent offenses such as aggravated assault, rape, arson, among others. Part II crimes include simple assault, prostitution, gambling, fraud, and other non-violent offenses.Please note that this is a very large dataset. To see all incidents, download all datasets for all years.If you are comfortable with APIs, you can also use the API links to access this data. You can learn more about how to use the API at Carto’s SQL API site and in the Carto guide in the section on making calls to the API.For questions about this dataset, contact publicsafetygis@phila.gov. For technical assistance, email maps@phila.gov.

Senior Crime Data based on Crime data for the City of Dallas for use in the web application below. Created in collaboration with the City of Dallas and the Senior Affairs Commission. Encompasses several variables (poverty, crime, marital status, etc.): Provide data for the Senior Demographics Applicationhttps://experience.arcgis.com/experience/d0f64d47090940f9aeeafce8cb1d1c4e. Crime Indices Pages:https://experience.arcgis.com/experience/d0f64d47090940f9aeeafce8cb1d1c4e/page/Crime-Indices/This data set is aggregated to the 2010 census tract level over a specific time period for the above application and may not be applicable for other analysis.

Current police beat boundaries in Chicago. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ), is required.

Senior Crime Data based on Crime data for the City of Dallas for use in the web application below. Created in collaboration with the City of Dallas and the Senior Affairs Commission. Encompasses several variables (poverty, crime, marital status, etc.): Provide data for the Senior Demographics Applicationhttps://experience.arcgis.com/experience/d0f64d47090940f9aeeafce8cb1d1c4e. Crime Indices Pages:https://experience.arcgis.com/experience/d0f64d47090940f9aeeafce8cb1d1c4e/page/Crime-Indices/This data set is aggregated to the 2010 census tract level over a specific time period for the above application and may not be applicable for other analysis.

Location Analytics Software Market Outlook

The global location analytics software market size was valued at approximately USD 12.8 billion in 2023 and is projected to reach USD 38.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.9% during the forecast period. This promising growth is driven by the increasing demand for spatial data and geographic information systems (GIS) across various industries, which has significantly enhanced decision-making capabilities and operational efficiencies.

One of the primary growth factors for the location analytics software market is the rising adoption of IoT (Internet of Things) devices. These devices generate massive amounts of location-based data, which can be analyzed to derive actionable insights. Industries such as retail, transportation, and logistics are leveraging location analytics to optimize routes, improve asset management, and enhance customer experiences. Additionally, the integration of advanced technologies like artificial intelligence (AI) and machine learning (ML) with location analytics software is expanding its application scope, thereby fueling market growth.

Another significant contributor to market growth is the increasing utilization of location analytics in government and public sector initiatives. Governments worldwide are adopting location-based services for urban planning, disaster management, and public health monitoring. The ability to visualize and analyze spatial data helps in making informed decisions and improving public services. For instance, location analytics played a crucial role during the COVID-19 pandemic by tracking the virus spread, managing quarantine zones, and allocating healthcare resources effectively.

The surge in e-commerce and the growing importance of omnichannel retail strategies are also propelling the demand for location analytics software. Retailers are using this technology to understand consumer behavior, optimize store locations, and enhance supply chain operations. Furthermore, the increasing focus on personalized marketing and customer relationship management (CRM) is driving retailers to invest in location analytics to gain insights into customer preferences and improve engagement. The ability to provide real-time location-based offers and promotions is becoming a key differentiator in the competitive retail landscape.

Regionally, North America is expected to hold the largest market share during the forecast period, driven by the presence of major technology companies and the early adoption of advanced analytical tools. The Asia Pacific region is anticipated to witness the highest growth rate due to the rapid digital transformation in countries like China and India. The expansion of smart city projects and the increasing investment in infrastructure development are significant factors contributing to the growth of the location analytics market in this region.

In recent years, the integration of Police Analytics Software has become increasingly vital in enhancing public safety and law enforcement operations. This software leverages location analytics to provide real-time insights into crime patterns, helping police departments allocate resources more effectively and respond swiftly to incidents. By analyzing spatial data, law enforcement agencies can identify high-crime areas, predict potential hotspots, and optimize patrol routes, thereby improving community safety. The ability to integrate data from various sources, such as surveillance cameras and social media, further enhances the capabilities of Police Analytics Software, enabling more comprehensive crime analysis and prevention strategies. As cities continue to grow and urban challenges become more complex, the adoption of such advanced analytics tools is expected to play a crucial role in modernizing policing efforts and building safer communities.

Component Analysis

The location analytics software market is segmented into software and services. The software segment includes various tools and platforms that enable the collection, analysis, and visualization of spatial data. This segment is expected to dominate the market due to the continuous advancements in software capabilities and the increasing integration with other enterprise systems. Organizations are investing in sophisticated location analytics software to derive deeper insights and improve decision-making processes. The software market is further categorized into standalone software and integrated softwar

The SanGIS data set includes an extensive collection of GIS maps that are available to the public.

 Application Data Included:

 1. Public Safety: Crime Mapping & Analysis, Computer Aided Dispatch,
 Emergency Response Planning

 2. Planning & Development: Specific Plans, Vegetation Mapping, Zoning,
 Geologic Hazards, Codes Enforcement

 3. Facilities Management: Water and Waste Water Utilities, Street
 Lighting, Storm Drains, Pavement Management

 4. Subdivision Mapping: Basemap Maintenance, Parcel Mapping, Survey
 Control, Orthophotography

 5. Route Management: Water Meter Readers, Trash & Recycling Routes

 6. Decision Support & Analysis: Facility Siting, Airport Noise, Slope
 Analysis, Demographics, Economic Development

 SanGIS was created in July, 1997, as a Joint Powers Agreement (JPA)
 between the City and County of San Diego. After 13 years of working
 together on data and application development, the City and County
 decided to formalize their partnership in GIS by creating the SanGIS
 JPA. Finding that access to correct and current geographic data was
 considered more important than application development to County and
 City departments, SanGIS focuses on ensuring geographic data is
 maintained and accessible.

 SanGIS Mission:

 To maintain and promote the use of a regional geographic data
 warehouse for the San Diego area and to facilitate the development of
 shared geographic data and automated systems which use that data.

 SanGIS Goals:

 1. To ensure geographic data currency and integrity.

 2. To provide cost effective access to geographic data to member
 agencies, subscribers and the public.

 3. To generate revenue from the sale of geographic data products to
 reduce the cost of map maintenance to member agencies.

 Data Collection:

 SanGIS data was created or obtained from several sources. Some of our
 data is licensed; some data was created from tabular digital files;
 some data was digitized from paper maps; and other data was entered
 using coordinate geometry tools.

 Updating the Data:

 Responsibility for the maintenance of the over 200 geographic data
 layers is distributed to City and County departments based on several
 factors such as who has the source documents, who has the greatest
 need for the data, and who is held accountable for this data as part
 of their city-wide or county-wide duties. Most basemap maintenance is
 completed by SanGIS staff. SanGIS is also responsible for coordinating
 with other data maintainers to ensure currency and accuracy for all
 participants.

 Data Coverage:

 All of the SanGIS geographic data is within San Diego County
 only. Much of our data covers the entire County of San Diego but some
 is only for the City of San Diego.

 [Summary provided by SanGIS]

This study tests the efficacy of an intervention--Safe Public Spaces (SPS) -- focused on improving the safety of public spaces in schools, such as hallways, cafeterias, and stairwells. Twenty-four schools with middle grades in a large urban area were recruited for participation and were pair-matched and then assigned to either treatment or control. The study comprises four components: an implementation evaluation, a cost study, an impact study, and a community crime study. Community-crime-study: The community crime study used the arrest of juveniles from the NYPD (New York Police Department) data. The data can be found at (https://data.cityofnewyork.us/Public-Safety/NYPD-Arrests-Data-Historic-/8h9b-rp9u). Data include all arrest for the juvenile crime during the life of the intervention. The 12 matched schools were identified and geo-mapped using Quantum GIS (QGIS) 3.8 software. Block groups in the 2010 US Census in which the schools reside and neighboring block groups were mapped into micro-areas. This resulted in twelve experimental school blocks and 11 control blocks which the schools reside (two of the control schools existed in the same census block group). Additionally, neighboring blocks using were geo-mapped into 70 experimental and 77 control adjacent block groups (see map). Finally, juvenile arrests were mapped into experimental and control areas. Using the ARIMA time-series method in Stata 15 statistical software package, arrest data were analyzed to compare the change in juvenile arrests in the experimental and control sites. Cost-study: For the cost study, information from the implementing organization (Engaging Schools) was combined with data from phone conversations and follow-up communications with staff in school sites to populate a Resource Cost Model. The Resource Cost Model Excel file will be provided for archiving. This file contains details on the staff time and materials allocated to the intervention, as well as the NYC prices in 2018 US dollars associated with each element. Prices were gathered from multiple sources, including actual NYC DOE data on salaries for position types for which these data were available and district salary schedules for the other staff types. Census data were used to calculate benefits. Impact-evaluation: The impact evaluation was conducted using data from the Research Alliance for New York City Schools. Among the core functions of the Research Alliance is maintaining a unique archive of longitudinal data on NYC schools to support ongoing research. The Research Alliance builds and maintains an archive of longitudinal data about NYC schools. Their agreement with the New York City Department of Education (NYC DOE) outlines the data they receive, the process they use to obtain it, and the security measures to keep it safe. Implementation-study: The implementation study comprises the baseline survey and observation data. Interview transcripts are not archived.

Senior Crime Data based on Crime data for the City of Dallas for use in the web application below. Created in collaboration with the City of Dallas and the Senior Affairs Commission. Encompasses several variables (poverty, crime, marital status, etc.): Provide data for the Senior Demographics Applicationhttps://experience.arcgis.com/experience/d0f64d47090940f9aeeafce8cb1d1c4e. Crime Indices Pages:https://experience.arcgis.com/experience/d0f64d47090940f9aeeafce8cb1d1c4e/page/Crime-Indices/This data set is aggregated to the 2010 census tract level over a specific time period for the above application and may not be applicable for other analysis.

Use the Slider template to display historical, live, or future data using any time interval. App users can play or move a time slider to animate data based on numeric values or changes over time to see how the data evolves. Examples: Display the migration patterns of endangered species to support conservation efforts. Animate crime data based on the day of the month that an event occurred. Create an app that displays an interactive weather forecast. Data requirements The Slider template requires a feature layer with at least one numeric field. To use the time option, the web map must contain a time-enabled layer. Key app capabilities Numeric slider - Configure sliders to animate data based on a numeric field in one or more layers. Time slider - Configure a time slider to show changes in data over time. Simplify the appearance of the slider by excluding markers and labels. Select slider mode - Choose how features are shown as the slider progresses. Live and forecast time settings - Enable the live option to show data for the specified period of time for live and recent data. Bookmarks - Allow users to zoom and pan to a collection of preset extents that are saved in the map. Export - Capture an image (PDF, JPG, or PNG) from the app that a user can save. Language switcher - Provide translations for custom text and create a multilingual app. Home, Zoom controls, Legend, Layer List, Search Supportability This web app is designed responsively to be used in browsers on desktops, mobile phones, and tablets. We are committed to ongoing efforts towards making our apps as accessible as possible. Please feel free to leave a comment on how we can improve the accessibility of our apps for those who use assistive technologies.