11 webinar courses on crime mapping in ArcGIS Pro created by Professor Grant Dwarve (University of Arkansas). Covers all necessary techniques for crime pattern analysis. See series on the Crime & Security Data Analytics Lab YouTube page. See also a recently published book Modern Policing Using ArcGIS Pro. Of interest to those moving to ArcGIS or an agency where this is the predominant GIS software.

QGIS for crime analysis. A guide to various crime mapping techniques applied in the open-source software QGIS. A brief walkthrough with curated datasets to practice. For police and public safety analysts without access to commercial GIS, those interested in open-source alternatives and those interested in learning geospatial techniques.

16 courses on crime mapping in R created by Dr Matt Ashby (Lecturer in Security and Crime Science, UCL). Covers a range of topics suitable for crime and intelligence analysis including hotspot maps, risky facilities and spatial-temporal patterns. See more on crimemapping package. See also Matt's resource site here http://lesscrime.info/

A light touch walkthrough guide on the steps taken for analysts when tasked with identifying and developing a visible hotspot policing project. 

A walkthrough guide on the steps taken for calculating location quotients for crime around different types of facilities. 

Walkthrough guides can be viewed in in RPubs or GitHub. Open source datasets used, OSM and Baltimore Police Department Part 1 Offenses.

Walkthrough guides can be viewed in RPubs or GitHub. Open source datasets used. Useful for strategic crime analysis.

Walkthrough guides can be viewed from the following links (view in RPubs, view in GitHub). Open source datasets used. Useful for tactical and strategic crime analysis, and performance analysts. Includes Jerry Ratcliffe's crime dispersion and aoristic tools, and Martin Andresen and Wouter Steenbeck spatial point pattern test. 

An overview of near-repeat victimisation, complete with two resources. The first resource is a slide-deck showing the various options for analysts wishing to undertake near-repeat analysis, and the second is a walked-through guide using NearRepeat and ptools in R.