From individual incidents to statistically significant spatial patterns.
This project examines how reported motor vehicle crime patterns change when the same incident dataset is analyzed through administrative aggregation, continuous density, local spatial statistics, and transportation proximity.
A list of 15,149 incident locations does not, by itself, reveal where a city's motor vehicle crime pattern is concentrated. This independent GIS research project builds a connected six-map workflow in ArcGIS Pro 3.6 that answers that question from four distinct analytical angles: how incidents are distributed once summarized to police districts and neighborhoods, where continuous concentration is elevated regardless of boundaries, where clustering is statistically significant rather than simply visually dense, and how closely incidents fall to San Francisco's major-road network.
Analytical layers were standardized in NAD 1983 UTM Zone 10N (EPSG:26910), providing a consistent projected coordinate system for density, spatial-statistics, and distance workflows. The project follows a complete GIS workflow — data preparation, QA/QC, spatial analysis, cartographic design, and technical documentation — and is delivered as six final map products plus a 31-page technical report.
Key Findings
What four analytical methods show about the same dataset.
Each finding below comes from a different spatial method. Read together, not in isolation, they form the project's central analytical argument.
Incident Distribution
15,149
Mapped motor vehicle crime incidents were included in the final analytical dataset, San Francisco, 2021–2025.
Administrative Aggregation
2,599
Bayview recorded the highest displayed police-district total, followed by Ingleside (2,212) and Mission (2,088). Full district breakdown in Map 02 below.
Density & Hot Spots
Central / NE
Kernel Density and Getis-Ord Gi* both identify the central and northeastern portion of San Francisco — particularly around Tenderloin, Mission, and nearby central districts — as a major area of elevated spatial concentration.
Density and statistical significance are related but not equivalent measures.
Road Proximity
58.7%
Of mapped incidents occurred within 250 m of the nearest major road (30.2% within 100 m; 28.5% between 101–250 m).
A spatial association, not a causal claim.
Analytical Story
Six maps, one incident dataset, four spatial questions.
The series moves from raw incident points to administrative aggregation, continuous density, statistically significant clustering, and transportation proximity — each method answering a distinct research question. Select any map to view it enlarged.
01Baseline Map
Incident Distribution
Question
Where are reported incidents located?
Method
Point feature visualization of all 15,149 mapped incidents against police-district and city-boundary context layers.
View full map ↗
What it shows
Incidents are distributed throughout the city but are more densely packed in central, northeastern, and southeastern areas, with western districts showing a more dispersed pattern.
Why it matters
Establishes the raw spatial baseline before any aggregation, smoothing, or statistical modeling is applied.
Point MappingCartographic Design
02Administrative Aggregation
Motor Vehicle Crime by Police District
Question
How do incident totals vary across police districts?
Method
Spatial Join of incident points to the ten SFPD district polygons, classified with five-class Natural Breaks (Jenks).
View full map ↗
Displayed District Totals
Bayview2,599
Ingleside2,212
Mission2,088
Northern1,704
Southern1,589
Taraval1,529
Central1,197
Richmond776
Park775
Tenderloin674
Interpretation
These are raw mapped incident counts, not population-normalized crime rates. Districts vary in size, land use, and street density, so totals should be interpreted alongside neighborhood, density, and hot-spot results — not as a standalone risk measure.
Spatial JoinNatural Breaks (Jenks)
03Administrative Aggregation
Motor Vehicle Crime by Neighborhood
Question
What finer-scale spatial variation appears when incidents are summarized by neighborhood instead of police district?
Method
A second Spatial Join aggregates the same incidents to San Francisco's neighborhood polygons, again classified with five-class Natural Breaks (Jenks).
View full map ↗
Why geography matters. Changing the aggregation unit reveals local variation that can be obscured by larger police-district boundaries — a practical example of the Modifiable Areal Unit Problem: the same events can look different depending on the boundaries used to summarize them.
Why it matters
Demonstrates that administrative results are scale-dependent, not a fixed property of the underlying data.
Spatial JoinNatural Breaks (Jenks)
04Continuous Density
Motor Vehicle Crime Density
Question
Where does incident concentration remain high regardless of administrative boundaries?
Method
Kernel Density estimation over the mapped incidents, masked to the San Francisco boundary.
View full map ↗
50 mCell Size
500 mSearch Radius
PlanarMethod
km²Area Units
What it shows
The strongest continuous concentration is centered around Tenderloin and extends into adjacent portions of Central, Southern, and Mission; western districts are generally lower and more dispersed.
Density models concentration on a continuous surface. It is not a statistical-significance test and should not be read as a probability or causal surface.
Kernel DensityRaster Analysis
05Statistical Clustering
Motor Vehicle Crime Hot Spots
Question
Where does statistically significant spatial clustering occur — as distinct from cells that are simply high in value?
Method
Getis-Ord Gi* Hot Spot Analysis run on a crime-analysis fishnet.
View full map ↗
90%Confidence
95%Confidence
99%Confidence
What it shows
Statistically significant hot-spot clustering appears primarily in central and northeastern San Francisco, with additional significant clustering in parts of Bayview, while cold spots are more common across western San Francisco.
Gi* evaluates whether high or low values cluster spatially more strongly than expected under the analysis model. A high-value cell alone is therefore not necessarily a statistically significant hot spot.
Getis-Ord Gi*Spatial Statistics
06Proximity Analysis
Motor Vehicle Crime Near Major Roads
Question
How close are mapped incidents to San Francisco's major-road network?
Method
Near analysis (planar distance) from each incident to the nearest major road, classified into distance bands.
View full map ↗
58.7%Within 250 m
30.2%0–100 m
28.5%101–250 m
20.9%251–500 m
20.4%>500 m
This identifies a spatial association and a transportation-corridor pattern worthy of further investigation — it does not demonstrate that roads cause motor vehicle crime.
Near AnalysisBuffer Analysis
Methods & Workflow
From raw records to interpretable spatial evidence.
Each stage is a discrete, checkable step in ArcGIS Pro 3.6, moving from descriptive mapping toward increasingly transformed spatial representations.
01
Acquire & review source data
Reviewed SFPD incident reports published through DataSF and municipal boundary, police-district, neighborhood, and road reference layers.
02
Prepare incident records
Filtered the broader incident source to the 2021–2025 motor vehicle crime subset and retained the fields needed for analysis.
03
Validate geometry and attributes
Reviewed field names, null values, and geographic attributes; separated authoritative base layers from derived analytical outputs.
04
Project analysis layers to NAD 1983 UTM Zone 10N
Standardized all distance- and density-based analysis in EPSG:26910 so results are measured in consistent meter units.
05
Aggregate incidents by administrative geography
Ran Spatial Join against police districts and neighborhoods; classified results with five-class Natural Breaks (Jenks).
06
Model continuous density
Generated a Kernel Density surface at 50 m cell size and 500 m search radius, masked to the San Francisco boundary.
07
Test local spatial clustering
Built a crime-analysis fishnet and ran Getis-Ord Gi* Hot Spot Analysis at 90%, 95%, and 99% confidence.
08
Measure proximity to major roads
Ran Near analysis on a derived analysis copy, classified NEAR_DIST into distance bands, and summarized percentages with Summary Statistics.
09
Validate analytical outputs
Checked spatial-join counts, distance calculations, and Gi* outputs against the overall incident total; documented QA items rather than forcing reconciliation.
10
Design and export final cartographic products
Applied a consistent portfolio layout — legend, north arrow, dual-unit scale bar, key finding — across all six final maps.
Analytical Parameters
Configuration, not just button-clicking.
Each analytical method depends on configuration choices that shape the resulting output. Documenting them here supports reproducibility and technical review.
Kernel Density
Cell size
50 m
Search radius
500 m
Method
Planar
Area units
Square kilometers
CRS
EPSG:26910
Hot Spot Analysis
Statistic
Getis-Ord Gi*
Input
Crime-analysis fishnet
Confidence
90%, 95%, 99%
CRS
EPSG:26910
Road Proximity
Method
Near (planar distance)
Categories
0–100 m · 101–250 m · 251–500 m · >500 m
Buffers
100 m · 250 m · 500 m
Aggregation
Method
Spatial Join
Targets
Police districts · Neighborhoods
Summary
Summary Statistics
Classification
Natural Breaks (Jenks), 5 classes
QA/QC
Checking the analysis instead of assuming the output is correct.
Every analytical output was checked against the source incident total rather than taken at face value.
QA note: the police-district Spatial Join accounts for 15,143 of the 15,149 mapped incidents. The six-record difference is retained as a documented spatial-assignment QA item rather than being artificially reconciled.
Geometry review
Attribute review
Coordinate-system verification
Projected-distance validation
Spatial-join count checks
Summary-statistics review
Raster-output inspection
Gi* output review
Near-distance review
Map-layout review
Cross-method interpretation
Interpretation
One dataset. Different spatial questions.
The same 15,149 incidents produce four different — and complementary — analytical answers, depending on the method used to examine them.
Administrative Counts
Where are incidents assigned once summarized to a police district or neighborhood boundary?
Density
Where is incident concentration elevated continuously across space, independent of any boundary?
Statistical Clustering
Where are high or low spatial values clustered more strongly than the selected spatial model would expect by chance?
Proximity
How close are mapped incidents to San Francisco's major-road network?
Counts ≠ Density ≠ Statistical Hot Spots ≠ Proximity
Methodological Scope & Limitations
What this project demonstrates — and what it doesn't.
This project demonstrates a retrospective, descriptive and exploratory spatial analysis of publicly available motor vehicle crime data: administrative aggregation, continuous density estimation, local spatial statistics, and transportation-proximity analysis in ArcGIS Pro, with documented QA/QC throughout.
Incident locations reflect the source data's reporting, geocoding, and privacy-protection practices (DataSF maps incidents to nearby intersections rather than exact coordinates).
DataSF's incident-location mapping process changed on April 24, 2024, which should be considered when interpreting fine-scale spatial patterns across the full 2021–2025 study period.
Police-district and neighborhood totals are raw mapped incident counts, not population-normalized crime rates.
Administrative aggregation results depend on the boundaries used to summarize incidents and will change if those boundaries change.
Kernel Density output depends on the selected cell size and search radius.
Getis-Ord Gi* results depend on the fishnet and spatial-relationship configuration used to define neighboring cells.
Road-proximity analysis identifies spatial association, not causation.
This is retrospective, descriptive and exploratory spatial analysis — it is not predictive policing and not a live public-safety system.
It should not be interpreted as an assessment of individual people or of any specific neighborhood.
It is an independently produced research project. It is not a claim of employment by the City of San Francisco, work commissioned by DataSF, or deployment by the San Francisco Police Department.
Full Report
Explore the full GIS research report.
The complete report documents the source data, GIS workflow, analytical parameters, results, interpretation, QA/QC, limitations, references, and supporting tables behind the six-map analysis.
This project demonstrates an end-to-end GIS workflow: preparing and validating spatial data, managing projected coordinate systems, performing vector and raster analysis, applying spatial statistics, validating analytical outputs, designing professional cartography, and communicating defensible findings and limitations.
Data Preparation & QA/QC
Preparing spatial data for analysis, validating geometry and attributes, checking coordinate systems, and reconciling analytical outputs against source totals.
Spatial Data PreparationCoordinate SystemsQA/QCSummary Statistics
Spatial Analysis
Using vector, raster, aggregation, and proximity methods to answer distinct geographic questions from the same incident dataset.
Distinguishing descriptive spatial concentration from statistically significant clustering and documenting the parameters used to interpret each result.
Kernel DensityGetis-Ord Gi*Spatial Statistics
Cartography & Communication
Turning analytical outputs into consistent professional map products, explaining results clearly, documenting limitations, and communicating findings without overstating what the analysis supports.
ArcGIS ProCartographic DesignTechnical DocumentationAnalytical Communication
GIS Analysis · Spatial Statistics · Cartography
Explore the complete methodology and technical documentation, or continue to my GIS experience and project work.
