Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/172000
Authors: 
Kinne, Jan
Resch, Bernd
Year of Publication: 
2017
Series/Report no.: 
ZEW Discussion Papers 17-063
Abstract: 
While the effects of non-geographic aggregation on inference are well studied in economics, research on geographic aggregation is rather scarce. This knowledge gap together with the use of aggregated spatial units in previous firm location studies result 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 demonstrate that the model yields plausible predictions and OSM data is suitable for microgeographic location analysis. Our results also show that non-aggregated data can be used to detect information on location determinants, which are superimposed when aggregated spatial units are analysed, 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.
Subjects: 
Firm Location
Location Factors
Software Industry
Microgeography
OpenStreetMap (OSM)
Prediction
Volunteered Geographic Information (VGI)
JEL: 
R12
L86
R30
Persistent Identifier of the first edition: 
Document Type: 
Working Paper
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