Parcourir par auteur "Grekousis, G."
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Analyzing High-Risk Emergency Areas with GIS and Neural Networks: The Case of Athens, Greece
Grekousis, G.; Photis, Y. N. (2014)Any analysis of health service problems facing the world today must consider that these problems exist in a geographic context. This fact has led to an increased need for accurate and current information to support emergency ... -
Comparison of two fuzzy algorithms in geodemographic segmentation analysis: The fuzzy C-means and Gustafson-Kessel methods
Grekousis, G.; Thomas, H. (2012)Clustering techniques are frequently used to analyze census data and obtain meaningful large-scale groups. Geodemographic segmentation involves classifying small geographic areas e for example, block groups, census tracts, ... -
Erratum to: A fuzzy index for detecting spatiotemporal outliers (Geoinformatica, (2011), 10.1007/s10707-011-0145-4)
Grekousis, G.; Photis, Y. N. (2012) -
A fuzzy index for detecting spatiotemporal outliers
Grekousis, G.; Fotis, Y. N. (2012)The detection of spatial outliers helps extract important and valuable information from large spatial datasets. Most of the existing work in outlier detection views the condition of being an outlier as a binary property. ... -
Locational planning for emergency management and response: An artificial intelligence approach
Photis, Y. N.; Grekousis, G. (2012)The efficiency of emergency service systems is measured in terms of their ability to deploy units and personnel in a timely and effective manner upon an event's occurrence. When dealing with public sector institutions, ... -
Modeling urban evolution using neural networks, fuzzy logic and GIS: The case of the Athens metropolitan area
Grekousis, G.; Manetos, P.; Photis, Y. N. (2013)This paper presents an artificial intelligence approach integrated with geographical information systems (GISs) for modeling urban evolution. Fuzzy logic and neural networks are used to provide a synthetic spatiotemporal ...