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Co-location Pattern Mining of Geosocial Data to Characterize Urban Functional Spaces


Scalable and Secure Big Data I

3D Reconstruction in Canonical

Class Agnostic Image Common Ob
Abstract


Spatial Co-location Pattern (SCP) mining continues to play a critical role in understanding the morphology of urban functional spaces of world cities. It requires a large amount of fine-granular data and computing efficiency to handle the combinatorial explosion of co-location patterns. To this end, this work has two main contributions - i) We showcase a novel approach to perform SCP mining to characterize intra-city scale structure of urban functionality or co-located activity patterns using geosocial Points-ofInterest (POI) vector data. ii) We present a generalized and optimized parallel/distributed SCP mining algorithm implemented on a Hadoop MapReduce system and demonstrate the utility of our approach using the city of Berlin (Germany) as an example. The SCPs tend to vary across Berlinā??s municipal boroughs and at different spatial scales. Our findings on Berlinā??s functional structure conform to existing urban geography models. Such a data-driven exploration of massive urban POIs using distributed computing is first of its kind and can help better understand the changing dynamics of urban functionality, as well as physical, and social network structure around the world

KeyWords
Spatial data mining, co-location pattern, MapReduce, distributed computing, urban areas



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