Chapter 1 Introduction

Urban planning in data-scarce contexts poses persistent challenges, particularly in rapidly growing cities of the Global South. Reliable demographic and economic data are often unavailable, outdated, or too aggregated to guide decisions effectively. This constrains planners’ ability to design inclusive and sustainable strategies, while the reliance on costly surveys or proprietary datasets delays decision-making and excludes many stakeholders from the planning process. In India, these difficulties are especially pronounced: fast-paced urbanisation has created mounting demands for housing, infrastructure, and services, yet planning institutions frequently lack the disaggregated evidence needed to address inequality and manage growth effectively. These conditions underline the urgency of developing alternative pathways to support planning through open and digital tools.

One promising direction is the use of remote sensing and open data. Datasets such as building footprints, points of interest (POI), road networks, and nighttime lights (NTL) are increasingly available at high resolution and consistent across cities. While such proxies cannot fully substitute conventional surveys, they provide valuable insights into settlement patterns and urban activities. At the same time, advances in spatial modelling and cloud-based platforms now enable these outputs to be delivered in interactive and transparent forms. Together, these developments open new opportunities to strengthen digital planning support in data-scarce contexts.

Jaipur, India, serves as the case study for this work. The city exemplifies both the promise and difficulty of planning in a data-scarce environment: it is expanding rapidly, faces pressures of infrastructure provision and inequality, yet lacks up-to-date and disaggregated demographic and economic data. By testing the feasibility of grid-level population estimation and constructing a composite index of economic activity from open data, Jaipur provides an ideal setting to demonstrate how digital planning support can be advanced where conventional evidence is sparse.

Against this backdrop, the central question of this study is: How can digital, open-access planning be supported in data-scarce contexts? To address this, two research questions are posed:

1. How can remote sensing and open data be used to estimate urban activity? This question is addressed in two strands:
   • Population estimation: using ward-level population counts as a baseline, a multiscale geographically weighted regression (MGWR) model is applied to generate grid-level population surfaces, capturing intra-urban variations that ward aggregates cannot reveal.
   • Economic Activity Index (EAI): a composite measure of urban economic vitality is constructed through principal component analysis (PCA), integrating indicators such as road density, POI density and NTL.
2. How can these analytical results be transformed into forms that support collaborative planning? This question concerns the design of a web-based tool for exploration and use by planners and communities.
   • A Google Earth Engine (GEE) app was developed to allow interactive querying of population estimates, including polygon-based selection.
   • The app also aggregates the EAI by ward, enabling users to compare relative economic activity across the city and to examine the contribution of underlying variables.

By tackling these questions, the study contributes to ongoing debates on urban data innovation in the Global South. It demonstrates how grid-level population estimates and composite indices of economic activity can be derived from remote sensing and open data, providing planners with alternative perspectives when up-to-date data is absent. Equally, by delivering outputs through an open-access web platform, it illustrates how digital tools can enhance transparency, accessibility, and collaboration in planning processes. While not substitutes for detailed surveys, these approaches can generate indicative surfaces that inform dialogue, experiments, and decision-making in resource-constrained environments.

The remainder of this thesis is structured as follows. Chapter 2 reviews literature on urban planning in data-scarce contexts, methods for population estimation and economic activity proxies, and the use of digital platforms for planning support. Chapter 3 then introduces the datasets and methods applied in this study, including grid-level population estimation, construction of the EAI, and implementation of a web application. Chapter 4 presents the results for Jaipur, highlighting both expected patterns and local discrepancies. Chapter 5 discusses the implications of these findings for planning support, as well as the limitations and potential directions for future research. Chapter 6 concludes by summarising the main contributions of this study.