Using GIS to Explore the Potential of Business Rating Data to Analyse Stock and Value Change for Land Administration: A Case Study of York

An open source delineation and hierarchical classification of UK retail agglomerations

Town centres and high streets typically form the social and commercial cores of UK cities and towns, yet, there is no uniform definition of what a town centre or high street is. In this study the spatial delineations of retail agglomerations are generated using open-source data for England, Wales, Scotland and Northern Ireland. The extent and boundaries of these physical retail areas are identified based on the density and connectivity patterns of individual retail units over space. A high resolution hexagonal grid is superimposed over spatial clusters of retail points and a network-based algorithm used to identify mutually exclusive tracts. Agglomerations are then pruned and fine-tuned according to a series of heuristic rules. Our retail agglomerations represent local commerce areas with shopping amenities and are assigned to a hierarchical classification ranking from the largest Regional Centres, Major Town Centres and Town Centres, down to Small Local Centres and Retail Parks. The classification into one of eleven hierarchies is based on a combination of relative rank in the local area and absolute size of retail units within the area. These retail agglomeration boundaries, hierarchical classification and lookups form an open-source spatial data product available for wide use and research implementation.

Measurement(s)	Retail Environment
Technology Type(s)	Processed Data
Factor Type(s)	Retail unit locations
Sample Characteristic - Environment	usage of an environment for retail activity
Sample Characteristic - Location	United Kingdom

Towards Attaining Sustainable Retail Property Locations: The Relationships between Supply, Demand, and Accessibility of Retail Spaces

This study explored retail location performance of cities by investigating relationships between changes in retail property stock (supply), changes in retail rental value (demand), and spatial accessibility (retail consumer movement) across three UK cities, namely, Leeds, Newcastle, and York. This is to understand how retail locations and assets can be managed sustainably. In this sense, sustainability was considered through a dual focus in this paper: (1) the efficient use of retail property assets for economic purposes and (2) the impact of these physical retail assets on the local environment in terms of carbon footprint. The study relied on space syntax ideology in computing spatial accessibility index and adopted business rate datasets in computing changes in retail rental value and stock. Findings showed that spatial accessibility across retail locations could predict the performance of retail rental value (but not stock) across the sampled cities. The study further showed that extent of city analysis (scale) is significant in estimating retail location performance and understanding the influence of accessibility. This evidence has the potential to facilitate better decision-making concerning the planning, design, and management of retail locations and spaces. The study is significant because it can serve as a reference for promoting an urban sustainability agenda, especially in ensuring that urban land and properties are used optimally to maximise their social, economic, and environmental values.

An Innovative GIS-Based Territorial Information Tool for the Evaluation of Corporate Properties: An Application to the Italian Context

The financial transmission of the USA’s housing price bubble has highlighted the inadequacy of the valuation methods adopted by the credit institutions, due to their static nature and inability to understand complex socio-economic dynamics and their related effects on the real estate market. The present research deals with the current issue of using Automated Valuation Methods for expeditious assessments in order to monitor and forecast market evolutions in the short and medium term. The paper aims to propose an evaluative model for the corporate market segment, in order to support the investors’, the credit institutions’ and the public entities’ decision processes. The application of the proposed model to the corporate real estate segment market of the cities of Rome and Milan (Italy) outlines the potentialities of this approach in property big data management. The elaboration of input and output data in the GIS (Geographic Information System) environment allowed the development of an intuitive platform for the immediate representation of the results and their easy interpretation, even to non-expert users.