A Conceptual Framework for Uncertainty Analysis in Map-Based Urban Spatial Planning

Abstract

Geographical information system (GIS) is a technology that can be used to support detailed spatial planning. High spatial resolution is required to display complex, intricate spatial forms, and heterogeneous dimensions. However, the application of GIS in spatial planning still has problems, including uncertainty. Uncertainty is a description of the problem that comes from imperfection. This paper presents a conceptual framework for the systematic analysis of GIS-based spatial uncertainty. To date, such analyzes have been rarely or satisfactorily performed. The conceptual model consists of three domains in which the main potential sources of uncertainty are systematically exposed. The first domain is the production-oriented uncertainty inherent in administrative boundary issues and zoning rules. Vagueness and ambiguity represent suitable concepts at this stage. Transformation-oriented uncertainty as a second domain occurs due to the zoning topology digital editing and processing. Thus, the corresponding uncertainty concept is an error. The third domain is application-oriented uncertainty. Validation of land use is needed so that spatial planning can be efficient by comparing application data with conditions in the Field. Error is a suitable concept to represent the uncertainty in the domain. Based on the research results, the framework can potentially access the complex nature of uncertainty and characterize the most relevant concepts for analysis. Uncertainty in geographic information at this time should be considered as an integral part that must be built into our knowledge. Anyone using uncertain information needs to think carefully about possible sources of uncertainty and how they can be handled.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 4 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.