Spatial Analysis

Overview

Three spatial questions I wanted answered well enough to act on. For each, I framed the question, gathered or scraped the data, and ran the analysis. I report what the data supported, including the times my hunch turned out wrong. The work is mostly ArcGIS Pro and Python (arcpy, pandas), with ESA SNAP for the radar processing.

SAR flood mapping: how close are the flood maps?

Question. During a real high-water event in the Coquille River valley (Coos County, OR), how well do FEMA’s flood-hazard maps, the ones flood insurance is priced against, match where the water actually went?

Data. Free Sentinel-1 synthetic-aperture radar (SAR), with ALOS-2 PALSAR and Landsat 9 for reference, plus FEMA’s published flood-hazard zones. Several scenes were mosaicked over the valley.

Method. I processed the SAR in ESA SNAP (orbit correction, radiometric calibration, terrain flattening, and terrain correction to handle the valley’s hills), then classified open water with ESRI’s pretrained Water Body Extraction (SAR) deep-learning model in ArcGIS Pro, and compared the detected water extent on the flood weekend against the mapped flood zones.

What it showed. In the Coquille valley the FEMA flood zones tracked the observed flooding closely. The valley-floor floodplain is a long-recognized, well-mapped feature, so for this area the maps and the radar largely agreed: the flood-zone lines weren’t far off from reality.

Water quality and school performance: does one track the other?

Question. Across Oregon, is there a spatial relationship between drinking-water quality and standardized school test scores, or does any apparent link really stand in for something else?

Data. Test-score and school data from the Oregon Department of Education; water-quality data I scraped and curated from an Oregon water-system source, where inspectors assign escalating demerit scores for code violations (a higher score means dirtier water); and US Census data for population density and income.

Method. I aggregated each dataset to regions and symbolized water score against test score as a bivariate choropleth, looking for spatial correlation.

What it showed. The direct relationship between water quality and test scores was weak. The pattern lined up better with density and income. Schools in eastern Oregon could post lower scores while differing enormously in resources, so funding and wealth explained far more of the variation than water quality did. The interesting part was assembling the question from three separate public sources, including water data that existed publicly but had to be scraped and hand-curated to be usable.

Electrification and grid reliability: do electrifying neighborhoods lose power more?

Question. Across Portland’s electric distribution areas, do the places under the most electrification pressure (EV adoption, heat-pump and electrical permits, housing densification) also have worse grid reliability? The question came from noticing more frequent short outages in my North Portland neighborhood as multi-unit housing and EV chargers went in, against the rare outages in the deeper Southeast where I’d lived before.

Data. Around 80,000 geocoded Portland residential building permits (city open data), tract-level EV registrations from the ODOT dashboard, PGE’s RE-113 reliability filings to the Oregon PUC (SAIDI/SAIFI), PGE feeder polygons and infrastructure attributes, Pacific Power circuit linework and reliability, ACS housing-unit estimates, and TIGER census tracts.

Method. An eight-step ArcGIS Pro and pandas pipeline. Pacific Power doesn’t publish feeder boundaries, so I built approximate service areas from circuit linework using Thiessen (Voronoi) polygons. I area-weighted the tract-level EV and housing data onto feeders, then scored every service area on two independent axes, transition pressure and grid stress, and combined them with a geometric mean so that only areas elevated on both ranked highly.

What it showed. Across 548 scored service areas the two axes were essentially uncorrelated (Spearman rho around 0.05): electrification pressure and poor reliability don’t track each other system-wide, which was the opposite of my starting hunch. But the framework still isolated ten specific overlap hotspots worth an engineering look (the Sylvan-Barnes feeder in the west hills and the Vernon substation area in Northeast Portland among them), and four of the five PGE picks were already flagged as worst-performing circuits. The useful output wasn’t a correlation. It was a repeatable way to target where the two problems actually coincide.