ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-2, 85-90, 2016
https://doi.org/10.5194/isprs-annals-III-2-85-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
02 Jun 2016
MINING CO-LOCATION PATTERNS FROM SPATIAL DATA
C. Zhou1, W. D. Xiao1,2, and D. Q. Tang1,2 1Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, 410073 Changsha, China
2Collaborative Innovation Center of Geospatial Technology, 430072 Wuhan, China
Keywords: Itemset mining, Spatial data , Co-location Abstract. Due to the widespread application of geographic information systems (GIS) and GPS technology and the increasingly mature infrastructure for data collection, sharing, and integration, more and more research domains have gained access to high-quality geographic data and created new ways to incorporate spatial information and analysis in various studies. There is an urgent need for effective and efficient methods to extract unknown and unexpected information, e.g., co-location patterns, from spatial datasets of high dimensionality and complexity. A co-location pattern is defined as a subset of spatial items whose instances are often located together in spatial proximity. Current co-location mining algorithms are unable to quantify the spatial proximity of a co-location pattern. We propose a co-location pattern miner aiming to discover co-location patterns in a multidimensional spatial data by measuring the cohesion of a pattern. We present a model to measure the cohesion in an attempt to improve the efficiency of existing methods. The usefulness of our method is demonstrated by applying them on the publicly available spatial data of the city of Antwerp in Belgium. The experimental results show that our method is more efficient than existing methods.
Conference paper (PDF, 852 KB)


Citation: Zhou, C., Xiao, W. D., and Tang, D. Q.: MINING CO-LOCATION PATTERNS FROM SPATIAL DATA, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-2, 85-90, https://doi.org/10.5194/isprs-annals-III-2-85-2016, 2016.

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