Mapping and Spatial Analysis of Crime- Open Software Edition

Uncover the foundations of crime analysis by distinguishing quantitative and qualitative variables, calculating crime rates to compare risk, and examining distributions through histograms, skewness, and normal versus nonnormal shapes.

Identify the map’s purpose, audience, and theme to guide design, layout, and data sources. Apply classification methods—equal interval, quantile, natural breaks (Jenks)—for 3–7 readable classes.

Explore data classification methods for crime mapping, including equal interval, natural breaks, quintiles, and pretty breaks, while calculating homicide rates and building descriptive statistics, histograms, and multi-panel maps.

Learn to use spatial joins to aggregate county data to the state level, summing population and homicides to compute homicide rates per 100,000 and produce a state map.

Identify outliers using mean, median, and box plots; compare range and interquartile range; apply the one point five times the interquartile range (iqr) rule or c-scores to flag extreme values.

Identify extreme values in 1990 US homicide rates using GeoDa by creating box plots and standard deviation maps, saving results, and assembling a two-panel map with Open Street Map basemap.

Map the relative risk of homicide victims in the United States in 1990 using GeoDa, save the relative risk ratios, and adjust classification and colors for clear audience interpretation.

Georeference crime data by aligning coordinates with a reference coordinate system and understanding ellipsoid, datums, and projections. Use geocoding via address matching and linear referencing on street networks.

Change and compare world map projections in a lab session, exploring Mercator, Gold Peters projections, and Robinson projection, then save a permanent projection by exporting a new shapefile.

Geocode toxic release sites from the EPA data using the Kyrgyz plugin in QGIS, create shapefiles, and map total releases with proportional symbol sizing while addressing data type issues.

Georeference TRI sites using coordinates and build a county level map of toxic releases in New York by summing site releases per county and overlaying with TRI locations.

Learn to create heat maps in QGIS to visualize point density of robberies in New York City in 2018, using shapefiles and census tracts, adjusting radius and blending.

Explore advanced heat maps in QGIS using kernel density estimation, projecting crime data to a local coordinate system, and identifying hotspots through raster-to-vector conversion and density thresholds.

Identify statistically significant hotspots of robberies in NYC using Getis-Ord Gi* local cluster analysis, constructing adaptive spatial weight matrices and comparing with heatmaps to produce a final hotspot map.

Explore spatial interpolation and inverse distance weighted methods to predict crime and public health variables, contrast interpolation with extrapolation, apply the first law of geography, and assess accuracy with cross-validation.

Interpolate ground-level ozone across the United States using inverse distance weighted interpolation, select monitoring stations, create a US mask, and clip the raster to an Albers equal area conic projection.

Learn to use the zonal statistics tool to perform raster-to-polygon joins and calculate the average ozone level per county, with options for mean, median, and other statistics.

Explore quick osm, the open street map tool, to fetch roads, buildings, amenities, and rivers as shapefiles. Build axis maps to hospitals and other features using simple queries and extents.

Learn how to map bivariate relationships with Vivarium maps, multipotent maps, and multilayer maps, identify significant spatial correlations using Moran's I, and reduce data with principal component analysis.

Build a vivarium bivariate map of homicide rate and poverty in the United States by categorizing both variables into three levels and merging them as a by-mode category.

Learn to apply bivariate Moran’s I to map homicide and poverty across the United States using local Moran’s I, queen contiguity, and significance testing (p<0.05).

Perform principal component analysis to reduce unemployment, female headed households, poverty, and income into a single standardized component; interpret by eigenvalues and loadings to map resource deprivation.

Explore spatial-temporal analysis of crime data with multi-panel maps and animated maps. Identify hot spots, percent change, and space-time clusters using the space-time scan statistic.

Create a two-panel map showing homicide rate distributions for 1980 and 1990, using the same legend to reveal true temporal changes by county.

Create a percent change map of homicide rates for Florida counties from 1980–1990, select Florida counties, compute a change field, apply natural breaks, and note instability in small populations.

Create an animated map of illegal firework detonations in New York City from January 1, 2020 to August 2020 using Time Manager, layered maps, and weekly time frames.

Identify persistent, emerging, and oscillating homicide hotspots from 1980 to 1990 using a queen contiguity weight matrix and local Moran's clustering.

Identify statistically significant spatial-temporal clusters of illegal fireworks detonations in New York City (June 1–Aug 21, 2020) using a space-time scan statistic and SaTScan.

Learn how buffers and isochrone buffers define areas of influence and access, using multiple rings, dissolved boundaries, and web maps to analyze crime risk and service areas.

Use buffers to identify vape shops within a 1,000 feet radius of schools in Trenton, New Jersey, tally shops per school, and map targeted venues for a public health campaign.

Develop an access map for Rowan University by generating isochrone buffers for 15, 30, 45, and 60 minutes driving, demonstrating time-to-destination over distance using OpenStreetMap tools.

Create an interactive web map in QGIS using the web plugin to visualize county homicide rates across decades, with emerging and persistent hotspots and customizable basemaps.