Analyzing and Predicting Micro-Location Patterns of Software Firms

• Main Authors: Jan Kinne, Bernd Resch
• Format: Article
• Language: English
• Published: MDPI AG 2017-12-01
• Series: ISPRS International Journal of Geo-Information
• Subjects: firm location; location factors; software industry; microgeography; OpenStreetMap (OSM); prediction; Volunteered Geographic Information (VGI); Modifiable Areal Unit Problem (MAUP)
• Online Access: https://www.mdpi.com/2220-9964/7/1/1

While the effects of non-geographic aggregation on statistical inference are well studied in economics, research on the effects of geographic aggregation on regression analysis is rather scarce. This knowledge gap, together with the use of aggregated spatial units in previous firm location studies, results in a lack of understanding of firm location determinants at the microgeographic level. Suitable data for microgeographic location analysis has become available only recently through the emergence of Volunteered Geographic Information (VGI), especially the OpenStreetMap (OSM) project, and the increasing availability of official (open) geodata. In this paper, we use a comprehensive dataset of three million street-level geocoded firm observations to explore the location pattern of software firms in an Exploratory Spatial Data Analysis (ESDA). Based on the ESDA results, we develop a software firm location prediction model using Poisson regression and OSM data. Our findings offer novel insights into the mode of operation of the Modifiable Areal Unit Problem (MAUP) in the context of a microgeographic location analysis: We find that non-aggregated data can be used to detect information on location determinants, which are superimposed when aggregated spatial units are analyzed, and that some findings of previous firm location studies are not robust at the microgeographic level. However, we also conclude that the lack of high-resolution geodata on socio-economic population characteristics causes systematic prediction errors, especially in cities with diverse and segregated populations.