TY - GEN
T1 - Using linked open geo boundaries for adaptive delineation of functional urban areas
AU - Khalili, Ali
AU - van den Besselaar, Peter
AU - de Graaf, Klaas Andries
PY - 2018
Y1 - 2018
N2 - The concentration of people, companies, research organizations and other activities in urban areas is a key process in the development of economies and societies. In order to investigate how these urban systems function, the OECD (Organization for Economic Co-operation and Development) in collaboration with EC (European Commission) and Eurostat have introduced the concept of Functional Urban Areas (FUAs). FUAs consider a preliminary set of socio-economic and environmental factors and provide a basis for an agreed definition for measuring development of metropolitan areas. However, because FUAs are predefined they do not meet the need for designing policies and research questions involving different types of urban areas that are defined by weighting some factors more than others or by using additional factors. Therefore, providing an adaptive approach for dynamic and multi-faceted delineation of FUAs, rather than merely relying on a rigid schema with a fixed list of FUAs per country, allows to more flexibly reflect the socio-economic geography of where people live and work. This adaptive definition of FUAs demands integration of data from multiple up-to-date linked data sources. In this paper, we describe an approach and implementation for a Linked Open Geo-Data space, which combines openly available spatial and non-spatial resources on the Web to classify urban areas with the aim to more flexibly monitor and research urban development.
AB - The concentration of people, companies, research organizations and other activities in urban areas is a key process in the development of economies and societies. In order to investigate how these urban systems function, the OECD (Organization for Economic Co-operation and Development) in collaboration with EC (European Commission) and Eurostat have introduced the concept of Functional Urban Areas (FUAs). FUAs consider a preliminary set of socio-economic and environmental factors and provide a basis for an agreed definition for measuring development of metropolitan areas. However, because FUAs are predefined they do not meet the need for designing policies and research questions involving different types of urban areas that are defined by weighting some factors more than others or by using additional factors. Therefore, providing an adaptive approach for dynamic and multi-faceted delineation of FUAs, rather than merely relying on a rigid schema with a fixed list of FUAs per country, allows to more flexibly reflect the socio-economic geography of where people live and work. This adaptive definition of FUAs demands integration of data from multiple up-to-date linked data sources. In this paper, we describe an approach and implementation for a Linked Open Geo-Data space, which combines openly available spatial and non-spatial resources on the Web to classify urban areas with the aim to more flexibly monitor and research urban development.
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U2 - 10.1007/978-3-319-98192-5_51
DO - 10.1007/978-3-319-98192-5_51
M3 - Conference contribution
AN - SCOPUS:85051640191
SN - 9783319981918
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 327
EP - 341
BT - The Semantic Web: ESWC 2018 Satellite Events: Revised Selected Papers
A2 - Gangemi, Aldo
A2 - Gentile, Anna Lisa
A2 - Nuzzolese, Andrea Giovanni
A2 - Rudolph, Sebastian
A2 - Maleshkova, Maria
A2 - Paulheim, Heiko
A2 - Pan, Jeff Z.
A2 - Alam, Mehwish
PB - Springer/Verlag
T2 - 15th Extended Semantic Web Conference, ESWC 2018
Y2 - 3 June 2018 through 7 June 2018
ER -