Abstract

The lack of timely and disaggregated demographic and economic information often constrains urban digital planning in data-scarce contexts. This study examines the application of open and remote sensing data in generating alternative evidence for planning, using Jaipur, India, as a case study. Two complementary strands of analysis were developed: grid-level population estimation through Multiscale Geographically Weighted Regression (MGWR), and the construction of an Economic Activity Index (EAI) to capture spatial economic variation via principal component analysis (PCA) of indicators including built-up area, points of interest, and nighttime lights.

The results show that while the population model broadly reproduced settlement structures, it also revealed systematic biases. Meanwhile, the EAI provided a composite representation of urban economic intensity. Both outputs were delivered through a Google Earth Engine (GEE) web application, enabling planners to interact with estimates and underlying variables.

By demonstrating how open data and cloud-based tools can generate and share population and economic activity estimates, this study presents a practical and replicable pathway for expanding digital planning support in contexts where conventional statistics are limited.