Short description:
The location pattern of any industry is the product of a large number of
individual decisions. Industrial location analysis investigates these location
decisions and seeks to detect location determinants that trigger and influence
such decisions. These determinants are generally referred to as location
factors. A thorough understanding of the impact of location factors on firms’
location decisions and firm performance can have important implications for
stakeholders. Managers and entrepreneurs can integrate valuable information
into the decision-making process when choosing the location of a new venture. Some
location factor-firm relationships which are relevant at the macro level
(aggregate) may not be so at the micro level (ecological fallacy). Suitable
data for microgeographic analysis has become available only recently through
the emergence of Volunteered Geographic Information (VGI) and the increasing
availability of official (open) geodata.
Kinne and Resch (2018) combined
open geodata, Volunteered Geographic Information (VGI), and a comprehensive firm
dataset (the Mannheim Enterprise Panel - MUP) containing approximately three million
firm observations to empirically estimate the relationship between a set of
location factors and the number of local software firms in Germany (see
figure). They concluded that the
microgeographic level of analysis provided new insights into the firm site
selection process. However, they also pointed out the particular requirements
to the statistical model and the data employed in a microgeographic location
analysis, like the need for high resolution geodata, which was not available in
all domains. They showed that this problem was most severe in cities, which
often feature segregated populations and districts with very different
socio-economic profiles. In the context of this master thesis, the research
conducted by Kinne and Resch in Germany shall be extended to the geo-economic context of the USA, where
higher-resolution socio-economic geodata are available. Furthermore, a
comparison between the results for Germany and the USA shall be carried out.
The location pattern of any industry is the product of a large number of
individual decisions. Industrial location analysis investigates these location
decisions and seeks to detect location determinants that trigger and influence
such decisions. These determinants are generally referred to as location
factors. A thorough understanding of the impact of location factors on firms’
location decisions and firm performance can have important implications for
stakeholders. Managers and entrepreneurs can integrate valuable information
into the decision-making process when choosing the location of a new venture. Some
location factor-firm relationships which are relevant at the macro level
(aggregate) may not be so at the micro level (ecological fallacy). Suitable
data for microgeographic analysis has become available only recently through
the emergence of Volunteered Geographic Information (VGI) and the increasing
availability of official (open) geodata.
Literature:
Kinne, Jan und Bernd Resch
(2018), Analyzing and Predicting Micro-Location Patterns of Software Firms,
ISPRS International Journal of Geo-Information 7, 1. http://www.mdpi.com/2220-9964/7/1/1/pdf
Arifi, D., Resch, B., Kinne, J. and Lenz, D. (2023) Innovation in Hyperlink and Social Media Networks: Comparing Connection Strategies of Innovative Companies in Hyperlink and Social Media Networks. PLOS ONE, 18(3), pp. e0283372, DOI: https://doi.org/10.1371/journal.pone.0283372.
Rammer, Christian, Jan Kinne und Knut Blind (2019),
Knowledge Proximity and Firm Innovation: A Microgeographic Analysis for Berlin,
Urban Studies.
Start date: ASAP
Prerequisites/qualifications: experience with analysing web data and social media, interest in economic geography/economics, OpenStreetMap, Regression analysis (optional)
