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Public Safety CrimeTop 9 Best Predictive Policing Software of 2026
Top 9 predictive policing software options compared for teams evaluating vendor fit, use cases, and practical tradeoffs like alerts and risk scoring.
How we ranked these tools
Vendors set the topic, but our editors set the inclusion criteria and the evaluation framework before research starts.
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Framework: Features 40% · Ease 30% · Value 30%
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CrimeNtel is the best pick for mid-to-large departments targeting gang suppression because its proprietary risk scoring engine forecasts how members, alliances, and activity evolve, whereas Palantir Gotham fits large agencies with complex data ecosystems since its ontology-driven fusion ties people, events, and locations into dynamic predictive simulations.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
CrimeNtel
Proprietary gang risk scoring engine that predicts member activities and alliances
Best for: Mid-to-large police departments focused on gang suppression and urban crime prevention.
SoundThinking
Editor pickHybrid predictive analytics fused with acoustic gunshot detection for proactive, real-time crime intervention
Best for: Mid-to-large urban police departments seeking data-driven resource optimization and integration with acoustic detection systems.
Palantir Gotham
Editor pickOntology-driven data modeling that interconnects entities (people, events, locations) for dynamic, simulation-based crime predictions
Best for: Large-scale police departments with big budgets and complex data ecosystems needing enterprise-grade predictive analytics.
Comparison Table
CrimeNtel
specializedDelivers web-based crime mapping and predictive analytics for hotspot identification.
Proprietary gang risk scoring engine that predicts member activities and alliances
CrimeNtel is a web-based predictive policing platform designed for law enforcement agencies to analyze crime data, forecast hotspots, and track gang activities using AI-driven analytics. It integrates historical crime reports, social media, and inter-agency intelligence to generate real-time risk maps and alerts. The software facilitates secure data sharing among departments to enhance proactive policing strategies.
- +Robust predictive analytics with accurate hotspot forecasting
- +Comprehensive gang intelligence database
- +Secure, real-time inter-agency data sharing
- –Steep learning curve for non-technical users
- –High implementation costs for smaller agencies
- –Limited customization for non-gang related predictions
Best for: Mid-to-large police departments focused on gang suppression and urban crime prevention.
SoundThinking
specializedIntegrates gunshot detection with AI-driven predictive policing for real-time risk assessment.
Hybrid predictive analytics fused with acoustic gunshot detection for proactive, real-time crime intervention
SoundThinking's predictive policing software, primarily through its CrimeForecasting platform, leverages historical crime data, 911 calls, parolee locations, and environmental factors to generate accurate forecasts of crime hotspots and trends. It helps law enforcement agencies optimize patrol deployments, allocate resources proactively, and reduce response times to potential incidents. The tool integrates seamlessly with SoundThinking's ShotSpotter gunshot detection system for enhanced real-time situational awareness.
- +Robust data integration from multiple sources for precise crime forecasting
- +Proven effectiveness in reducing crime in deployed cities
- +Strong integration with real-time tools like ShotSpotter
- –High implementation and subscription costs
- –Risk of perpetuating biases in historical data
- –Requires significant training and data infrastructure setup
Best for: Mid-to-large urban police departments seeking data-driven resource optimization and integration with acoustic detection systems.
Palantir Gotham
enterpriseFuses disparate data sources for advanced predictive law enforcement intelligence.
Ontology-driven data modeling that interconnects entities (people, events, locations) for dynamic, simulation-based crime predictions
Palantir Gotham supports predictive policing by connecting structured records like incident reports with semi-structured intelligence and operational data through its data fusion workflows. The platform then applies analytics to surface crime patterns, forecast likely hotspots, and run scenario simulations for deployment planning. Geospatial views and interactive exploration help analysts validate model outputs against mapped events and jurisdictions.
A meaningful tradeoff is that Gotham’s predictive work depends on high-quality data preparation and agency-specific configuration rather than a plug-and-play workflow. Typical success shows up when agencies standardize entity resolution, align time windows across data sources, and define clear rules for risk scoring and intervention thresholds. It fits best for operational planning cycles that require both investigative context and map-based decision support.
- +Unparalleled data fusion across structured and unstructured sources for comprehensive crime prediction
- +Advanced ML models for accurate hotspot forecasting and anomaly detection
- +High-security compliance with granular access controls for sensitive law enforcement data
- –Extremely high cost limits accessibility for mid-sized agencies
- –Steep learning curve requiring specialized training and on-site engineers
- –Risk of algorithmic bias and privacy controversies in predictive applications
Police analytics teams
Forecast patrol hotspots from fused incident data
More targeted patrol deployments
Fusion center operators
Link suspects, locations, and evidence timelines
Faster evidence correlation
Show 1 more scenario
Command staff
Simulate resource shifts for risk reduction
Better operational planning
Command users run scenario simulations to compare intervention plans and resource allocations before deployment.
Best for: Large-scale police departments with big budgets and complex data ecosystems needing enterprise-grade predictive analytics.
PredPol
specializedForecasts crime hotspots and times using machine learning on historical police data.
Self-exciting point process algorithm (adapted from seismology) that models crime as contagious 'aftershocks' for precise spatiotemporal predictions
PredPol is a pioneering predictive policing software that uses a proprietary machine learning algorithm, inspired by earthquake aftershock models, to analyze historical crime data and generate daily hotspot predictions on interactive maps divided into 500x500 foot boxes. These predictions help law enforcement agencies proactively deploy patrols to high-risk areas, aiming to prevent crimes like burglaries and thefts before they occur. Adopted by over 50 agencies across the US and UK, it claims significant crime reductions in tested deployments.
- +Highly accurate hotspot predictions with reported 10-20% crime reductions in burglaries and thefts
- +Intuitive map-based dashboard for quick tactical decisions
- +Seamless integration with existing crime databases like CAD/RMS systems
- –Algorithmic bias concerns leading to over-policing in minority neighborhoods
- –Heavy reliance on clean, comprehensive historical data which may not exist everywhere
- –Lack of full transparency in proprietary algorithms limits independent audits
Best for: Mid-to-large police departments with reliable crime data seeking to optimize patrol efficiency and reduce property crimes.
ArcGIS Crime Analysis
enterpriseGeospatial software for crime analysis, hotspot identification, and predictive public safety planning.
Geoprocessing-driven hotspot and pattern analysis that stays inside ArcGIS layers for repeatable operational runs.
ArcGIS Crime Analysis turns incident locations and attributes into spatiotemporal crime forecasting inputs using ArcGIS geospatial workflows and modeling tools. Core capabilities include hot-spot mapping, near-repeat style pattern analysis, and configurable prediction horizons that can feed patrol allocation decisions.
- +Deep GIS workflow support for incident mapping and spatiotemporal analysis
- +Configurable analysis parameters for neighborhood-level hotspot outputs
- +Strong integration with ArcGIS data management for recurring updates
- +Extensible geoprocessing for building tailored prediction inputs
- –Requires GIS and modeling discipline to get usable risk scoring
- –More setup time than single-purpose predictive policing tools
- –Person-based prediction workflows depend on how data is prepared
- –Direct validation against policing outcomes is not fully automated
Best for: Fits when GIS-centric agencies need crime forecasting workflows tied to their data pipeline and operational layers.
ATAC Workbench
vertical specialistLaw enforcement analytics software for crime analysis, mapping, and predictive allocation.
Workbench-driven forecasting runs that convert configured inputs into consistent risk surfaces for operational geography.
ATAC Workbench targets predictive policing teams that need risk-terrain style place-based analysis backed by case and incident evidence. It supports workflow-driven preparation of inputs and repeatable modeling runs so analysts can move from data preparation into forecasting outputs.
- +Workflow tooling for preparing inputs and standardizing model runs
- +Place-based risk-terrain style outputs designed for operational geography
- +Configuration supports repeatable forecasting cycles for analysts
- +Integration support for law enforcement data sources and records
- –Forecast tuning demands analyst time and governance discipline
- –Limited out-of-the-box guidance for validation metrics like precision and recall
- –Spatiotemporal granularity depends on how inputs are structured
- –Automation beyond interactive modeling can lag behind API-first systems
Best for: Fits when analytics teams need repeatable place-based forecasting workflows tied to evidence, not ad-hoc mapping.
ESRI ArcGIS
enterpriseProvides geospatial analytics and machine learning for crime pattern prediction.
Emerging Hot Spot Analysis for identifying new, intensifying, or persistent crime trends over time
ESRI ArcGIS is a comprehensive GIS platform that supports predictive policing through advanced spatial analytics, crime mapping, and hotspot identification. It enables law enforcement to analyze historical crime data, detect patterns using tools like Getis-Ord Gi* statistics, and integrate with machine learning for forecasting potential crime areas. While versatile for various industries, its spatiotemporal analysis tools make it adaptable for data-driven policing strategies.
- +Powerful spatial statistics and hotspot analysis for crime pattern detection
- +Seamless integration with external data sources and ML models
- +Scalable for enterprise-level deployments with real-time mapping
- –Steep learning curve requiring GIS expertise
- –High cost for full functionality
- –Not specialized solely for predictive policing, leading to feature bloat
Best for: Large law enforcement agencies with GIS-trained staff needing robust spatial analytics for crime forecasting.
SAS Public Safety
enterpriseOffers advanced statistical modeling for crime forecasting and resource optimization.
Hyper-local crime forecasting using patented visual analytics and ensemble ML models
SAS Public Safety is an enterprise-grade analytics platform from SAS Institute designed for law enforcement agencies, leveraging advanced AI, machine learning, and big data analytics to enable predictive policing. It forecasts crime hotspots, optimizes patrol routes, and supports risk assessments by integrating diverse data sources like historical crime records, social media, and sensor data. The solution provides interactive visualizations and scenario modeling to aid decision-making and resource allocation in public safety operations.
- +Powerful machine learning for accurate crime prediction and hotspot analysis
- +Seamless integration with large-scale data sources and enterprise systems
- +Customizable models and visualizations tailored to agency needs
- –Steep learning curve requiring data science expertise
- –High implementation and licensing costs
- –Complex setup for smaller agencies without IT support
Best for: Large metropolitan police departments with data analysts and budgets for enterprise analytics.
Splunk Enterprise Security
enterpriseAnalyzes big data streams for real-time threat detection and crime prediction.
Machine Learning Toolkit (MLTK) integrated for real-time predictive modeling and anomaly detection on crime data
Splunk Enterprise Security (ES) is a powerful SIEM platform designed primarily for cybersecurity but adaptable to predictive policing through its advanced data analytics and machine learning capabilities. It ingests and correlates vast datasets from sources like crime reports, sensors, social media, and public records to identify patterns, forecast crime hotspots, and generate risk scores. Custom dashboards and automated alerting enable law enforcement to proactively allocate resources based on predicted threats.
- +Advanced machine learning for anomaly detection and crime forecasting
- +Highly scalable for massive data volumes from multiple sources
- +Rich visualization and customizable dashboards for operational insights
- –Steep learning curve requiring skilled analysts
- –High implementation and licensing costs
- –Not purpose-built for policing, needing significant customization
Best for: Large police departments with data science expertise looking to adapt enterprise SIEM for predictive analytics.
Conclusion
After evaluating 9 predictive policing software, CrimeNtel stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
How to Choose the Right Predictive Policing Software
Predictive policing software uses historical crime incident data and calls-for-service feeds to generate crime forecasting outputs that teams can use for directed patrol and resource deployment decisions. This guide covers ArcGIS Crime Analysis, ATAC Workbench, CrimeNtel, ESRI ArcGIS, Palantir Gotham, PredPol, SAS Public Safety, SoundThinking, and Splunk Enterprise Security.
The tools differ in where prediction logic runs and how outputs map to operational workflows. ArcGIS Crime Analysis and ESRI ArcGIS focus on geoprocessing inside ArcGIS layers for operational geography, while PredPol applies a self-exciting point-process approach that drives spatiotemporal hotspot predictions from recurring patterns.
Predictive Policing Software for place-based and person-based crime forecasting workflows
Predictive policing software supports crime forecasting by processing spatiotemporal crime incident data and producing hotspot or risk surfaces that guide patrol allocation. Some systems focus on place-based risk terrain outputs, where ATAC Workbench turns configured inputs into repeatable forecasting runs for operational geography.
Other platforms emphasize broader data modeling and simulation for crime prediction across entities like people, events, and locations, where Palantir Gotham uses ontology-driven connections to support enterprise-scale predictive analytics. In practice, agencies also need governance controls that align prediction usage with audit log expectations and RBAC-style access boundaries across analysts, planners, and supervisors.
Predictive Policing Software capabilities that affect workflow, control, and repeatability
Prediction outputs only change operations when the platform controls how crime incident data and calls-for-service feeds turn into actionable hotspot or risk surfaces. The main differentiators show up in where the prediction logic runs and how repeatable runs are configured for operational geography.
The buying focus should also track governance controls that constrain who can use forecasts and how teams audit prediction-driven decisions. Tools such as ArcGIS Crime Analysis and ESRI ArcGIS support geoprocessing workflows, while PredPol uses a self-exciting point-process approach that pushes spatiotemporal hotspots for directed patrol decisions.
Operational geoprocessing workflow and hotspot run repeatability
ArcGIS Crime Analysis and ESRI ArcGIS run geoprocessing inside ArcGIS layers so operational teams can reproduce hotspot outputs from configured analysis parameters. ATAC Workbench similarly emphasizes workbench-driven forecasting runs that standardize model inputs into consistent risk surfaces for operational geography.
Entity-centric data fusion and simulation-oriented prediction modeling
Palantir Gotham uses ontology-driven data modeling to connect people, events, and locations for dynamic, simulation-based crime predictions. Splunk Enterprise Security instead packages predictive modeling into an enterprise ML toolkit that plugs into real-time security analytics workflows.
Tactical outputs tied to resource deployment and near-real-time intervention
PredPol produces an intuitive map-based dashboard that supports quick tactical decisions from spatiotemporal crime predictions. SoundThinking combines hybrid predictive analytics with acoustic gunshot detection to support proactive, real-time crime intervention workflows.
Disciplinary support for gang-specific prediction use cases
CrimeNtel delivers a proprietary gang risk scoring engine that predicts member activities and alliances with outputs backed by a gang intelligence database. This focus changes implementation needs compared with place-based hotspot tools that primarily optimize geographic prediction horizons.
Machine learning tooling for scalable anomaly detection alongside forecasting
Splunk Enterprise Security pairs an ML toolkit for real-time predictive modeling with anomaly detection on crime data at high throughput. SAS Public Safety uses ensemble ML models and patented visual analytics to support hyper-local forecasting and analyst review of risk outputs.
Choose by prediction philosophy, data pipeline fit, and the governance controls needed for operational use
The decision should start with the prediction philosophy that matches the agency workflow, because place-based forecasting tools behave differently from entity-model and simulation-oriented platforms. ArcGIS Crime Analysis and ESRI ArcGIS emphasize GIS-first analysis runs that stay inside ArcGIS layers for repeatable operations, while Palantir Gotham emphasizes ontology-driven data fusion across structured and unstructured sources.
The second decision axis should be how teams will operationalize outputs into directed patrol or intervention actions without creating uncontrolled adoption. PredPol and SoundThinking drive tactical workflows through map dashboards or real-time acoustic integration, while ArcGIS-first and workbench-driven tools tend to demand more analyst time for configuration and validation disciplines.
Match the prediction engine to the operational geography model used in planning
If operational geography is already managed through ArcGIS layers, ArcGIS Crime Analysis and ESRI ArcGIS fit because their workflows produce hotspot outputs that stay within those layers. If operational planning depends on configured runs that standardize model inputs into consistent risk surfaces, ATAC Workbench aligns with workbench-driven forecasting runs.
Select the platform philosophy for data fusion across entities or signals
If prediction needs to connect people, events, and locations through an ontology for enterprise simulation-style analytics, Palantir Gotham is built for that modeling shape. If prediction is intended to run inside an enterprise analytics and monitoring environment with real-time anomaly detection, Splunk Enterprise Security provides an ML toolkit integrated into security analytics workflows.
Plan for the workflow that turns forecasts into patrol allocation or intervention actions
If the agency needs tactical map outputs that support quick decisions, PredPol centers on an intuitive map-based dashboard tied to spatiotemporal hotspot predictions. If the agency needs proactive intervention tied to acoustic gunshot detection, SoundThinking combines predictive analytics with gunshot sensing for near-real-time use.
Account for implementation complexity tied to audience and data availability
If the agency has GIS-trained staff and accepts setup time for usable risk scoring, ArcGIS Crime Analysis and ESRI ArcGIS rely on modeling discipline to turn GIS parameters into effective risk outputs. If the agency has limited analyst bandwidth and wants guidance on validation metrics, SAS Public Safety and ATAC Workbench can still require specialist time, and ATAC Workbench specifically notes limited out-of-the-box guidance for precision and recall style validation.
Choose a specialized use case platform only when the use case demands it
If gang suppression is the core objective and the agency can support a gang intelligence database workflow, CrimeNtel targets member activity and alliance prediction. If the primary objective is place-based crime forecasting or hotspot planning, gang-focused scoring introduces unnecessary governance and onboarding load.
Who Predictive Policing Software is for based on data readiness and operational workflow fit
Predictive policing software fits agencies that can convert historical crime incident patterns and calls-for-service feeds into repeatable forecast runs that support directed patrol decisions. ArcGIS-centric teams get a direct path with ArcGIS Crime Analysis and ESRI ArcGIS because operational geography remains in ArcGIS layers, while workbench-focused analytics teams get repeatable forecasting runs with ATAC Workbench.
Some platforms fit only when the agency can support specialized prediction workflows, such as gang risk scoring with CrimeNtel or real-time intervention integration with SoundThinking. Enterprise data ecosystems with structured and unstructured sources fit Palantir Gotham, while SIEM-like environments with skilled analysts can use Splunk Enterprise Security for anomaly detection plus predictive modeling.
GIS-centric agencies with ArcGIS layers and GIS-trained staff
ArcGIS Crime Analysis and ESRI ArcGIS generate hotspot outputs through geoprocessing inside ArcGIS layers and require modeling discipline for usable risk scoring.
Analytics teams that want repeatable place-based risk surface runs
ATAC Workbench focuses on configured inputs and standardized forecasting runs that turn into consistent risk surfaces designed for operational geography.
Large enterprise environments building entity-centric prediction and simulation workflows
Palantir Gotham uses ontology-driven data modeling to interconnect people, events, and locations for comprehensive crime prediction across complex data ecosystems.
Urban departments prioritizing proactive intervention tied to acoustic sensing
SoundThinking combines hybrid predictive analytics with acoustic gunshot detection to support proactive, real-time crime intervention and resource optimization.
Departments with gang-focused suppression programs
CrimeNtel centers on a proprietary gang risk scoring engine that predicts member activities and alliances using a comprehensive gang intelligence database.
Common pitfalls when buying Predictive Policing Software
A frequent failure pattern is assuming predictive outputs will be operationally usable without matching the platform to the agency’s data workflow and configuration discipline. ArcGIS Crime Analysis and ESRI ArcGIS can require GIS modeling discipline, and ATAC Workbench can demand forecast tuning time for governance-grade repeatability.
Choosing a prediction platform without aligning it to the agency’s operational geography workflow
ArcGIS Crime Analysis and ESRI ArcGIS stay inside ArcGIS layers, so agencies that do not manage operational geography in ArcGIS will face extra translation work before patrol allocation can use forecasts.
Underestimating configuration and validation effort for risk scoring accuracy
ATAC Workbench supports workflow tooling for standardizing model runs, but it also calls out forecast tuning demands and limited out-of-the-box guidance for validation metrics like precision and recall.
Treating tactical dashboards as substitutes for data completeness and governance discipline
PredPol depends heavily on clean, comprehensive historical crime data, and missing or inconsistent inputs can push hotspot outputs toward misleading patterns that then drive over-optimized patrol decisions.
Buying gang-focused prediction without a gang intelligence operating model
CrimeNtel is built around gang intelligence and member activity prediction, so agencies that cannot support the gang database workflow increase implementation burden without gaining fit to place-based patrol objectives.
Ignoring bias and fairness risks during deployment planning
PredPol includes documented algorithmic bias concerns that can lead to over-policing in minority neighborhoods, and SoundThinking also flags risk of perpetuating biases in historical data.
How analysts verified these Predictive Policing Software entries
Analysts opened the original publication and copied each stated figure next to the passage where it appears. Analysts compared those figures against official documentation and the software changelog, and they used an independent review only when one was cited in the publication.
Analysts kept a figure only when it matched the referenced documents or a cited benchmark source. Analysts then wrote the buyer guide advisory sections to reflect the capabilities described in those matched product figures.
Frequently Asked Questions About Predictive Policing Software
How do predictive policing platforms turn incident history into near-term forecasts?
Which tools handle spatiotemporal analysis inside a GIS workflow?
How do agencies connect predictive outputs to CAD, mapping, and operational dashboards?
What API and integration paths are typical for law enforcement data ecosystems?
How do these systems control user permissions and audit activity for sensitive records?
What data migration work is required when replacing legacy crime mapping or reporting systems?
When does a predictive policing workflow break down due to data latency or missing fields?
Where do model validation and evaluation metrics get handled, and what gets measured?
Which configuration choices most affect forecast granularity and operational usability?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
Funding and competing interests
This study was co-funded by several of the vendors assessed in it. No single vendor funded it. Funders could propose the market studied.
They had no role in selecting vendors, setting or weighting criteria, analysing data or drawing conclusions, and they received the same factual-accuracy check as every other vendor assessed.
Market definition, inclusion rules and criteria weights were fixed by an editor before research began and did not change afterwards. Editorial process →
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