spatial cluster detection · offline geocoding
MESA is a desktop application for spatial epidemiology. It geocodes case addresses and detects spatial clusters in the resulting point data, running the geocoder and the statistics in-process. No network access is required, and case data remains on the local machine.
windows x64 · macos apple silicon · linux x64
Figure 1. Getis-Ord Gi* on MESA's demo dataset, computed offline.
Local hot- and cold-spot detection with Getis-Ord Gi* and a Bernoulli spatial scan, reported alongside a global Moran's I autocorrelation diagnostic. Inference is Monte-Carlo and reproduces bit-for-bit across platforms.
MESA builds a local SQLite address index that the geocoder matches against, for any US state, from the National Address Database (USDOT, public domain) — downloaded directly from the source and never redistributed. You can also load your own locality's authoritative points, and optional supplemental files extend coverage.
Reads Cognos MHT, CSV, and Excel exports. Columns can be corrected, tables joined on shared keys, and results exported to CSV, Excel, or spatial formats.
MESA emits no telemetry and opens no outbound connections while analyzing. Protected health information stays on the operator's machine.
The same offline geocoder can be exposed as a local ArcGIS GeocodeServer endpoint. ArcGIS Pro can batch-geocode tables against it directly, with no ArcGIS Enterprise and no tokens. It binds to localhost by default, so requests do not leave the machine.
A Cognos MHT, CSV, or Excel export is parsed and column-profiled, and missing fields are reported.
.mht · .csv · .xlsxThe address column is matched against the loaded authoritative address data. Each result carries a precision label.
operator-supplied authoritative dataGetis-Ord Gi* or a Bernoulli scan, with Monte-Carlo inference, identifies where cases aggregate into hot and cold spots.
Gi* · Bernoulli scanResults are written back to CSV, Excel, or a spatial format, precision labels included, for use in a downstream tool or a report.
.csv · .xlsx · spatialSpatial epidemiology usually means shipping addresses to a geocoding API. MESA doesn't. The address database lives on your disk and the analysis runs in-process, so you can work on an air-gapped machine and still get reproducible results. Every figure on this page, including the scan below, is real MESA output computed entirely offline.
Figure 2. Bernoulli spatial scan on MESA's demo dataset, validated against SaTScan.
The geocoder matches against authoritative data, the statistics are checked against the standard reference implementations, and every run reproduces byte for byte.
| ≈ 98 million | US National Address Database points MESA builds from, per state, matched entirely offline |
| PySAL · R/spdep | Getis-Ord Gi*, Moran's I, and multiple-testing correction cross-checked against the standard libraries |
| SaTScan 10.3.2 | Bernoulli spatial scan matched against the field standard, no discrepancies in the suite |
| bit-identical | the same p-values on Windows, macOS, and Linux, from one core or a hundred |
| ≈ 0.05 | empirical false-positive rate at α = 0.05 under complete spatial randomness, so Type-I error tracks the nominal level (checked against 99.999% Wilson intervals) |
| > 90% | true-positive rate at large effect size for an injected hot spot; statistical power rises monotonically with effect size |
| 10−4 | Gi* z-scores match PySAL's esda.G_Local within this tolerance, and adjusted p-values within 10−3 |
MESA geocodes anywhere in the US, building a local database for any state from the National Address Database, and is validated nationwide with the adversarial harness below — across eight representative states of differing address styles and data quality.
| 98.8% | sensitivity across eight states built from the US National Address Database: the correct address point recovered under adversarial input noise |
| 95.0% | specificity: fabricated addresses correctly refused rather than snapped to a wrong, distant point, on the same adversarial controls |
The adversarial harness scores each match against the record's own authoritative coordinate, with fabricated negative controls and stratified bootstrap intervals; not a synthetic self-benchmark. The figures cover eight states (Alaska, Connecticut, DC, Delaware, Nebraska, Rhode Island, Wisconsin, Wyoming) built from the National Address Database; the scoring was fit on separate development data and applied unchanged, so these are held-out, out-of-distribution results. The specificity shown is the genuine wrong-street rate; the full per-state numbers, both raw and genuine, and the method are in the published validation data, which MESA refreshes as each National Address Database release is validated. Because the geocoder works against whatever authoritative data you load, results depend on that data's coverage and quality, which MESA reports per state before you download. Spatial-statistics cross-validation re-runs in CI against regenerated PySAL, R, and SciPy oracles.
Full methodology, parameters, and citations: geocoding, Gi* & Moran's I, Bernoulli scan, or the methods overview.
Actively developed: recent work adds k-neighborhood and correction-method sensitivity analysis, so a result can be checked against alternate specifications, plus further cross-platform reproducibility hardening.
Native builds for each platform. Every release is published with a SHA-256 checksum so you can verify it before running.
| platform | architecture | status | |
|---|---|---|---|
| Windows | x64 · Windows 10/11 · .exe | coming soon | Download |
| macOS | Apple Silicon · arm64 · .dmg | coming soon | Download |
| Linux | x64 · AppImage | coming soon | Download |
Per-platform builds are still being set up; this table will show the current version and checksums once they are published. MESA is pre-release — for early access, contact ross@spatialepi.org.
MESA is developed by Ross Wardrup, an MPH Epidemiology candidate at the University of Memphis, with a background in geospatial software and public-health surveillance and an epidemiology practicum at the Southeast Utah Health Department. Publications: ORCID · Google Scholar · GitHub · LinkedIn
MESA