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

Arif Masrur; Gautam Thakur; Kevin Sparks; Rachel Palumbo; Donna J. Peuquet

doi:10.1109/BigData47090.2019.9006263

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

Arif Masrur, Gautam Thakur, Kevin Sparks, Rachel Palumbo, Donna J. Peuquet

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Scopus citations

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.

Original language	English
Title of host publication	Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
Editors	Chaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4099-4102
Number of pages	4
ISBN (Electronic)	9781728108582
DOIs	https://doi.org/10.1109/BigData47090.2019.9006263
State	Published - Dec 2019
Event	2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States Duration: Dec 9 2019 → Dec 12 2019

Publication series

Name	Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

Conference

Conference	2019 IEEE International Conference on Big Data, Big Data 2019
Country/Territory	United States
City	Los Angeles
Period	12/9/19 → 12/12/19

Funding

This manuscript was co-authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government and the publisher, by accepting the article for publication, acknowledge that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan.

Keywords

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

Access to Document

10.1109/BigData47090.2019.9006263

Cite this

Masrur, A., Thakur, G., Sparks, K., Palumbo, R., & Peuquet, D. J. (2019). Co-location Pattern Mining of Geosocial Data to Characterize Urban Functional Spaces. In C. Baru, J. Huan, L. Khan, X. T. Hu, R. Ak, Y. Tian, R. Barga, C. Zaniolo, K. Lee, & Y. F. Ye (Eds.), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 4099-4102). Article 9006263 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData47090.2019.9006263

Masrur, Arif ; Thakur, Gautam ; Sparks, Kevin et al. / Co-location Pattern Mining of Geosocial Data to Characterize Urban Functional Spaces. Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. editor / Chaitanya Baru ; Jun Huan ; Latifur Khan ; Xiaohua Tony Hu ; Ronay Ak ; Yuanyuan Tian ; Roger Barga ; Carlo Zaniolo ; Kisung Lee ; Yanfang Fanny Ye. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 4099-4102 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019).

@inproceedings{addc100ce1c34f80a4194cf18d5f1abf,

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.",

keywords = "MapReduce, Spatial data mining, co-location pattern, distributed computing, urban areas",

author = "Arif Masrur and Gautam Thakur and Kevin Sparks and Rachel Palumbo and Peuquet, \{Donna J.\}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Big Data, Big Data 2019 ; Conference date: 09-12-2019 Through 12-12-2019",

year = "2019",

month = dec,

doi = "10.1109/BigData47090.2019.9006263",

language = "English",

series = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "4099--4102",

editor = "Chaitanya Baru and Jun Huan and Latifur Khan and Hu, \{Xiaohua Tony\} and Ronay Ak and Yuanyuan Tian and Roger Barga and Carlo Zaniolo and Kisung Lee and Ye, \{Yanfang Fanny\}",

booktitle = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",

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Masrur, A, Thakur, G , Sparks, K , Palumbo, R & Peuquet, DJ 2019, Co-location Pattern Mining of Geosocial Data to Characterize Urban Functional Spaces. in C Baru, J Huan, L Khan, XT Hu, R Ak, Y Tian, R Barga, C Zaniolo, K Lee & YF Ye (eds), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019., 9006263, Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019, Institute of Electrical and Electronics Engineers Inc., pp. 4099-4102, 2019 IEEE International Conference on Big Data, Big Data 2019, Los Angeles, United States, 12/9/19. https://doi.org/10.1109/BigData47090.2019.9006263

Co-location Pattern Mining of Geosocial Data to Characterize Urban Functional Spaces. / Masrur, Arif; Thakur, Gautam ; Sparks, Kevin et al.
Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. ed. / Chaitanya Baru; Jun Huan; Latifur Khan; Xiaohua Tony Hu; Ronay Ak; Yuanyuan Tian; Roger Barga; Carlo Zaniolo; Kisung Lee; Yanfang Fanny Ye. Institute of Electrical and Electronics Engineers Inc., 2019. p. 4099-4102 9006263 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Co-location Pattern Mining of Geosocial Data to Characterize Urban Functional Spaces

AU - Masrur, Arif

AU - Thakur, Gautam

AU - Sparks, Kevin

AU - Palumbo, Rachel

AU - Peuquet, Donna J.

PY - 2019/12

Y1 - 2019/12

N2 - 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.

AB - 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.

KW - MapReduce

KW - Spatial data mining

KW - co-location pattern

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KW - urban areas

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DO - 10.1109/BigData47090.2019.9006263

M3 - Conference contribution

AN - SCOPUS:85081351246

T3 - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

SP - 4099

EP - 4102

BT - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

A2 - Baru, Chaitanya

A2 - Huan, Jun

A2 - Khan, Latifur

A2 - Hu, Xiaohua Tony

A2 - Ak, Ronay

A2 - Tian, Yuanyuan

A2 - Barga, Roger

A2 - Zaniolo, Carlo

A2 - Lee, Kisung

A2 - Ye, Yanfang Fanny

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conference on Big Data, Big Data 2019

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Masrur A, Thakur G , Sparks K , Palumbo R, Peuquet DJ. Co-location Pattern Mining of Geosocial Data to Characterize Urban Functional Spaces. In Baru C, Huan J, Khan L, Hu XT, Ak R, Tian Y, Barga R, Zaniolo C, Lee K, Ye YF, editors, Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 4099-4102. 9006263. (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). doi: 10.1109/BigData47090.2019.9006263

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

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