Automated identification and characterization of parcels (AICP) with OpenStreetMap and Points of Interest

TL;DR

This paper presents an automated method using OpenStreetMap and POI data to identify and characterize urban parcels in China, offering a resource-efficient alternative to traditional survey methods.

Contribution

It introduces a novel approach combining OSM road networks and POI data with a CA model for parcel identification, applicable at a national scale in China.

Findings

Identified 82,645 urban parcels across 297 Chinese cities.

Produced reasonably accurate parcel approximations compared to traditional methods.

Demonstrated the potential of open data for urban parcel analysis in resource-limited settings.

Abstract

Against the paucity of urban parcels in China, this paper proposes a method to automatically identify and characterize parcels (AICP) with OpenStreetMap (OSM) and Points of Interest (POI) data. Parcels are the basic spatial units for fine-scale urban modeling, urban studies, as well as spatial planning. Conventional ways of identification and characterization of parcels rely on remote sensing and field surveys, which are labor intensive and resource-consuming. Poorly developed digital infrastructure, limited resources, and institutional barriers have all hampered the gathering and application of parcel data in developing countries. Against this backdrop, we employ OSM road networks to identify parcel geometries and POI data to infer parcel characteristics. A vector-based CA model is adopted to select urban parcels. The method is applied to the entire state of China and identifies 82,645 urban parcels in 297 cities. Notwithstanding all the caveats of open and/or crowd-sourced data, our approach could produce reasonably good approximation of parcels identified from conventional methods, thus having the potential to become a useful supplement.