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Detecting city-hinterland pairs with a community detection algorithm
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Orientador(es)
Resumo(s)
With the increasing urbanization, cities tend to grow together with the infrastructure networks
beyond their administrative boundaries. Identifying the right dimension of the city is thus
challenging for the geographers. One way to represent a city could be identifying the city core and
its hinterland as a part of the city itself. Since the transport network is used to deliver the resources
between the city and its hinterland, we can hypothesize that the connectedness between the city
and its hinterland is represented by the transport network topology. Hence, it would be justifiable
to consider the transport network topology as a foundation for identifying hinterlands. One way to
achieve this task of identifying hinterland utilizing the transport network topology is by using
community detection algorithms. This thesis presents and evaluates a methodology for defining
city-hinterland pairs at a regional level using community detection algorithms applied to the
transport network. Easily accessible OpenStreetMap(OSM) road network data was used as the data
for our research. We used three modularity optimization based community detection algorithms to
identify communities in the road network. The identified communities were assigned to city core
areas and hinterland based on the proposed criteria. Results from different algorithms were
compared among each other and with the OECD Functional Urban Areas(FUAs). Similarity
among the results between the three algorithms was acceptable with the average Goodness-of-fit
(GOF) scores of 0.60 and 0.65 for core urban area and hinterland respectively. With OECD FUAs
results showed less similarity with average GOF scores of 0.40 and 0.31 for core urban area and
hinterland respectively. The disimilarity of our result with OECD data is justifiable as the OECD
FUAs use population data and commuting data of administrative units without considering
transport connectivity but we use only road network as the basis for identifying city and hinterland
communities.
Descrição
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
Palavras-chave
City Hinterland Transport Network Community Detection Algorithms Communities Modularity