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Statements

Subject Item
n2:RIV%2F61989100%3A27350%2F12%3A86080311%21RIV13-GA0-27350___
rdf:type
n11:Vysledek skos:Concept
dcterms:description
The study of inequality of socioeconomic phenomena in urban areas may provide useful recommendations for targeted social policy, local measurements, urban planning and investment strategies. Among various tools we select spatial heterogeneity indexes, spatial autocorrelation measures, the LISA analysis and the spatial hierarchical clustering. The most promising outputs we obtain from last two methods. LISA allows indicating seeds of segregation and detecting localities even not nominated by experts yet. The LISA analysis reveals clusters of anomalous values, but results are heavily influenced by neighbourhoods. The methods of spatial hierarchical clustering are implemented in a new database application which is under development. This application standardizes data, calculates measures of similarity among objects, applies various methods of aggregation and applies different spatial relationships. First results show the ability to identify homogeneous clusters inside selected localities; nevertheless a continuous extended research is required to optimise these methods. The study of inequality of socioeconomic phenomena in urban areas may provide useful recommendations for targeted social policy, local measurements, urban planning and investment strategies. Among various tools we select spatial heterogeneity indexes, spatial autocorrelation measures, the LISA analysis and the spatial hierarchical clustering. The most promising outputs we obtain from last two methods. LISA allows indicating seeds of segregation and detecting localities even not nominated by experts yet. The LISA analysis reveals clusters of anomalous values, but results are heavily influenced by neighbourhoods. The methods of spatial hierarchical clustering are implemented in a new database application which is under development. This application standardizes data, calculates measures of similarity among objects, applies various methods of aggregation and applies different spatial relationships. First results show the ability to identify homogeneous clusters inside selected localities; nevertheless a continuous extended research is required to optimise these methods.
dcterms:title
Methods of Spatial Clustering in a City Methods of Spatial Clustering in a City
skos:prefLabel
Methods of Spatial Clustering in a City Methods of Spatial Clustering in a City
skos:notation
RIV/61989100:27350/12:86080311!RIV13-GA0-27350___
n11:predkladatel
n16:orjk%3A27350
n3:aktivita
n14:P
n3:aktivity
P(GA403/09/1720)
n3:dodaniDat
n20:2013
n3:domaciTvurceVysledku
n13:4385772 n13:8041113 n13:1503278
n3:druhVysledku
n7:D
n3:duvernostUdaju
n19:S
n3:entitaPredkladatele
n8:predkladatel
n3:idSjednocenehoVysledku
149788
n3:idVysledku
RIV/61989100:27350/12:86080311
n3:jazykVysledku
n9:eng
n3:klicovaSlova
spatial autocorrelation; LISA; spatial hierarchical clustering; spatial heterogeneity
n3:klicoveSlovo
n4:spatial%20hierarchical%20clustering n4:spatial%20heterogeneity n4:spatial%20autocorrelation n4:LISA
n3:kontrolniKodProRIV
[8C51B1D57EF0]
n3:mistoKonaniAkce
Brno
n3:mistoVydani
Brno
n3:nazevZdroje
Geography and Geoinformatics: Challenge for Practise and Education : proceedings of 19th international conference : [Brno, September 8-9, 2011]
n3:obor
n22:AO
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
4
n3:projekt
n15:GA403%2F09%2F1720
n3:rokUplatneniVysledku
n20:2012
n3:tvurceVysledku
Horák, Jiří Soukup, Pavel Ivan, Igor Inspektor, Tomáš
n3:typAkce
n21:EUR
n3:zahajeniAkce
2011-09-08+02:00
s:numberOfPages
11
n5:hasPublisher
Masaryk University
n18:isbn
978-80-210-5799-9
n10:organizacniJednotka
27350