TY - GEN
T1 - Mining the city data
T2 - 8th Workshop on Self-Organizing Maps, WSOM 2011
AU - Neme, Omar
AU - Pulido, J. R.G.
AU - Neme, Antonio
N1 - Funding Information:
This research is derived from a project supported by Instituto de Ciencia y Tecnología del Distrito Federal, Mex. (ICyTDF), under contract PICCT08-55.
PY - 2011
Y1 - 2011
N2 - Cities are instances of complex structures. They present several conflicting dynamics, emergence of unexpected patterns of mobility and behavior, as well as some degree of adaptation. To make sense of several aspects of cities, such as traffic flow, mobility, social welfare, social exclusion, and commodities, data mining may be an appropriate technique. Here, we analyze 72 neighborhoods in Mexico City in terms of economic, demographic, mobility, air quality and several other variables in years 2000 and in 2010. The visual information obtained by self-organizing map shows interesting and previously unseen patterns. For city planners, it is important to know how neighborhoods are distributed accordingly to demographic and economic variables. Also, it is important to observe how neighbors geographically close are distributed in terms of the mentioned variables. Self-organizing maps are a tool suitable for planners to seek for those correlations, as we show in our results.
AB - Cities are instances of complex structures. They present several conflicting dynamics, emergence of unexpected patterns of mobility and behavior, as well as some degree of adaptation. To make sense of several aspects of cities, such as traffic flow, mobility, social welfare, social exclusion, and commodities, data mining may be an appropriate technique. Here, we analyze 72 neighborhoods in Mexico City in terms of economic, demographic, mobility, air quality and several other variables in years 2000 and in 2010. The visual information obtained by self-organizing map shows interesting and previously unseen patterns. For city planners, it is important to know how neighborhoods are distributed accordingly to demographic and economic variables. Also, it is important to observe how neighbors geographically close are distributed in terms of the mentioned variables. Self-organizing maps are a tool suitable for planners to seek for those correlations, as we show in our results.
KW - Self-organizing maps
KW - data mining
KW - urban analysis
KW - urbanism
UR - http://www.scopus.com/inward/record.url?scp=79959299889&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-21566-7_17
DO - 10.1007/978-3-642-21566-7_17
M3 - Contribución a la conferencia
SN - 9783642215650
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 168
EP - 177
BT - Advances in Self-Organizing Maps - 8th International Workshop, WSOM 2011, Proceedings
Y2 - 13 June 2011 through 15 June 2011
ER -