Chaitanya Jobanputra | Searcharxiv

arXiv2026

Multi-Scale Spatio-Temporal Exploratory Analysis of Urban Vehicle Theft

Vehicle theft is a persistent urban problem with large economic and social costs. We analyze 521,098 stolen-vehicle reports filed with the Los Angeles Police Department from 2000 to 2022, at scales from hours to decades and from 100 m grid cells to census tracts. Much of the work lies in making the administrative records fit for analysis: we separate thefts from recoveries and traffic reports filed in the same records, repair time fields, validate the geocoding, and harmonize census data across changing tract boundaries. Theft is strongly and persistently concentrated: 10.8% of 250 m cells hold half of all thefts, and six census tracts are significant rate hotspots in all 23 years after false discovery rate control. Thefts close in space and time are more frequent than expected by chance, but about half of this excess comes from events at identical coordinates. Lower-income tracts have more thefts at equal exposure, monthly temperature shows no association with theft, and thefts peak in the evening. From 2015-2019 to 2021-2022, the share of Kia and Hyundai among stolen vehicles rose from 1.5% to 9.5% and from 1.6% to 7.7%, mostly among model years 2011-2021, which largely lacked a standard immobilizer. All findings are descriptive. We also describe how the data quality issues we corrected would otherwise distort several of these patterns.

cs.CY↗

arXiv2026

Puffin: Probabilistic Learning of Spatial Detail From Coarse Observations

High-resolution socioeconomic variables are important for applications such as urban planning, public health, disaster response, and resource allocation. In practice, however, these variables are often observed only at a coarse spatial resolution. We introduce Puffin, a probabilistic framework for statistical disaggregation that raises the resolution of coarse totals using high-resolution satellite embeddings as covariates. Instead of predicting a single value for each fine-resolution subregion, Puffin learns a probability distribution and is trained through an aggregation-aware likelihood. At inference, Puffin conditions these predictions on the observed regional total and splits it among the subregions. The resulting fine-scale estimates are consistent with the observed aggregate and come with calibrated uncertainty, without requiring fine-resolution labels for training. We evaluate Puffin on German and US census, employment, and election data across population, jobs, and other count variables, and study when statistical disaggregation succeeds or fails across regions, countries, and targets.

cs.LG↗