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

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-of-Interest (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 Berlin's municipal boroughs and at different spatial scales. Our findings on Berlin'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.

Authors:
 [1]; ORCiD logo [2];  [2];  [2];  [1]
  1. Pennsylvania State University
  2. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1666009
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: The 4th IEEE International Workshop on Big Spatial Data (BSD 2019) - Los Angeles, California, United States of America - 12/9/2019 5:00:00 AM-12/12/2019 5:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Masrur, Arif, Thakur, Gautam, Sparks, Kevin, Palumbo, Rachel, and J peuquet, Donna. Co-location Pattern Mining of Geosocial Data to Characterize Urban Functional Spaces. United States: N. p., 2019. Web. doi:10.1109/BigData47090.2019.9006263.
Masrur, Arif, Thakur, Gautam, Sparks, Kevin, Palumbo, Rachel, & J peuquet, Donna. Co-location Pattern Mining of Geosocial Data to Characterize Urban Functional Spaces. United States. https://doi.org/10.1109/BigData47090.2019.9006263
Masrur, Arif, Thakur, Gautam, Sparks, Kevin, Palumbo, Rachel, and J peuquet, Donna. Sun . "Co-location Pattern Mining of Geosocial Data to Characterize Urban Functional Spaces". United States. https://doi.org/10.1109/BigData47090.2019.9006263. https://www.osti.gov/servlets/purl/1666009.
@article{osti_1666009,
title = {Co-location Pattern Mining of Geosocial Data to Characterize Urban Functional Spaces},
author = {Masrur, Arif and Thakur, Gautam and Sparks, Kevin and Palumbo, Rachel and J peuquet, Donna},
abstractNote = {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-of-Interest (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 Berlin's municipal boroughs and at different spatial scales. Our findings on Berlin'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.},
doi = {10.1109/BigData47090.2019.9006263},
url = {https://www.osti.gov/biblio/1666009}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {12}
}

Conference:
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