Applications of Data Mining and Text Mining in Urban Studies and Planning Research: A Systematic Review

Abstract

Rapid urbanization challenges traditional urban analysis methods, prompting increased reliance on data and text mining techniques. This systematic review evaluates 75 peer-reviewed articles (2015–2024), highlighting applications in governance, transportation, sustainability, and public engagement. Studies demonstrate benefits like improved policymaking through machine learning techniques (e.g., topic modeling, sentiment analysis) utilizing diverse datasets from social media and sensors. However, challenges remain, including methodological standardization, ethical issues, and spatial integration. Future research addressing these barriers will enhance the transformative potential of data-driven approaches, promoting sustainable, efficient, and equitable urban development.

Keywords

data mining text mining natural language processing urban studies urban planning machine learning big data

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References

Abdelrazek

Smith

Doe

. 2023. “Topic Modeling Algorithms and Applications: A Survey.” Information Systems 112: 102131. https://doi.org/10.1016/j.is.2022.102131.

Ali

Younas

. 2021. “Understanding and Interpreting Regression Analysis.” Evidence-Based Nursing 24: 116–8. https://doi.org/10.1136/ebnurs-2020-103374.

Barabási

A.-L.

Pósfai

. 2016. Network Science. Cambridge, UK: Cambridge University Press.

Batty

2013. “Big Data, Smart Cities and City Planning.” Dialogues in Human Geography 3 (3): 274–9. https://doi.org/10.1177/2043820613513390.

Blei, D. M., A. Y. Ng, and M. I. Jordan. 2003. “Latent Dirichlet Allocation.” Journal of Machine Learning Research 3, 993–1022.

Braun

Clarke

. 2006. “Using Thematic Analysis in Psychology.” Qualitative Research in Psychology 3 (2): 77–101. https://doi.org/10.1191/1478088706qp063oa.

Chang

Huang

Yao

Zhao

. 2022a. “How do new Transit Stations Affect People’s Sentiment and Activity? A Case Study Based on Social media Data in Hong Kong.” Transport Policy 120: 139–55. https://doi.org/10.1016/j.tranpol.2022.03.011.

Chang

Huang

Zhang

Chin

K.-S.

. 2022b. “Tracking Traffic Congestion and Accidents Using Social media Data: A Case Study of Shanghai.” Accident Analysis and Prevention 169, https://doi.org/10.1016/j.aap.2022.106618.

Engin

Dijk

J. V.

Lan

Longley

P. A.

Treleaven

P. C.

Batty

Penn

. 2019. “Data-driven Urban Management: Mapping the Landscape.” Journal of Urban Management 9: 140–50.

10.

Fang, Q., S. Zhu, D. Ma, L. Zhang, and S. Yang. (2019). “How Effective Is a Marine Spatial Plan: An Evaluation Case Study in China.” Ecological Indicators 98, 508–14. https://doi.org/10.1016/j.ecolind.2018.11.028 .

11.

Fayyad, U., G. Piatetsky-Shapiro, and P. Smyth. (1996). “From Data Mining to Knowledge Discovery in Databases.” AI Magazine, 17 (3), 37. https://doi.org/10.1609/aimag.v17i3.1230 .

12.

Feldman

Sanger

. 2007. The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge, UK: Cambridge University Press.

13.

Fontes

Murcos

Carneiro

Ribeiro

Rossetti

R. J. F.

. 2023. “Leveraging Social Media as a Source of Mobility Intelligence: An NLP-Based Approach.” IEEE Open Journal of Intelligent Transportation Systems 4: 663–81. https://doi.org/10.1109/OJITS.2023.3308210.

14.

2024. “Natural Language Processing in Urban Planning: A Research Agenda.” Journal of Planning Literature 39 (3): 395–407. https://doi.org/10.1177/08854122241229571.

15.

Sanchez

T. W.

Reu Junqueira

. 2024. “Deciphering Public Voices in the Digital Era: Benchmarking ChatGPT for Analyzing Citizen Feedback in Hamilton, New Zealand.” Journal of the American Planning Association 90 (4): 728–741. https://doi.org/10.1080/01944363.2024.2309259 .

16.

Ghahramani

Galle

N. J.

Ratti

Pilla

. 2021. “Tales of a City: Sentiment Analysis of Urban Green Space in Dublin.” Cities 119, https://doi.org/10.1016/j.cities.2021.103395.

17.

Hauthal

Burghardt

Poplin

. 2020. “The Use of Geospatial Technologies in the Analysis of Urban Systems.” In Comprehensive Geographic Information Systems, edited by Höfle

Yang

. Vol. 3, pp. 73–83. Oxford, UK: Elsevier.

18.

Healey

1997. Collaborative Planning: Shaping Places in Fragmented Societies. London, UK: Macmillan.

19.

Huai

Van de Voorde

. 2022. “Which Environmental Features Contribute to Positive and Negative Perceptions of Urban Parks? A Cross-Cultural Comparison Using Online Reviews and Natural Language Processing Methods.” Landscape and Urban Planning 218, https://doi.org/10.1016/j.landurbplan.2021.104307.

20.

Huang

Cervone

Zhang

. 2017. “A Cloud-Enabled Automatic Disaster Analysis System of Multi-Sourced Data Streams: An Example Synthesizing Social media, Remote Sensing and Wikipedia Data.” Computers, Environment and Urban Systems 66: 23–37. https://doi.org/10.1016/j.compenvurbsys.2017.06.004.

21.

Huang

Wei

Liu

Cui

Xie

Deng

. 2023. “Inspiring” Policy Transfer: Analysis of Urban Renewal in Four First-Tier Chinese Cities.” Land 12 (1), https://doi.org/10.3390/land12010118.

22.

Hummon

Doreian

. 1990. “Computational Methods for Social Network Analysis.” Social Networks 12 (4): 273–88. https://doi.org/10.1016/0378-8733(90)90011-W.

23.

Jain

A. K.

2010. “Data Clustering: 50 Years Beyond K-Means.” Pattern Recognition Letters 31 (8): 651–66. https://doi.org/10.1016/j.patrec.2009.09.011.

24.

Jeawak

S. S.

Jones

C. B.

Schockaert

. 2020. “Predicting the Environment from Social media: A Collective Classification Approach.” Computers, Environment and Urban Systems 82, https://doi.org/10.1016/j.compenvurbsys.2020.101487.

25.

Jehangir

Lee

Park

. 2023. “A Survey on Named Entity Recognition—Datasets, Tools, and Methodologies.” Natural Language Processing Journal 3: 100017. https://doi.org/10.1016/j.nlp.2023.100017.

26.

Kitchin

2014. “The Real-Time City? Big Data and Smart Urbanism.” GeoJournal 79 (1): 1–14. https://.doi.org/10.2139/ssrn.2289141.

27.

Koh

Y. F.

Loc

H. H.

Park

. 2022. “Towards a “City in Nature”: Evaluating the Cultural Ecosystem Services Approach Using Online Public Participation GIS to Support Urban Green Space Management.” Sustainability (Switzerland) 14 (3), https://doi.org/10.3390/su14031499.

28.

Kovacs-Györi

Tóth

Nagy

. 2020. “Opportunities and Challenges of Geospatial Analysis for Promoting Urban Livability in the era of big Data and Machine Learning.” ISPRS International Journal of Geo-Information 9 (12): 752. https://doi.org/10.3390/ijgi9120752.

29.

Lore

Harten

J. G.

Boeing

. 2024. “A Hybrid Deep Learning Method for Identifying Topics in Large-Scale Urban Text Data: Benefits and Trade-Offs.” Computers, Environment and Urban Systems 111, https://doi.org/10.1016/j.compenvurbsys.2024.102131.

30.

Low

Tekler

Z. D.

Cheah

. 2021. “An end-to-end Point of Interest (Poi) Conflation Framework.” ISPRS International Journal of Geo-Information 10 (11), https://doi.org/10.3390/ijgi10110779.

31.

Ma, D., B. Lv, X. Li, X. Li, and S. Liu. 2023a. “Heterogeneous Impacts of Policy Sentiment with Different Themes on Real Estate Market: Evidence from China.” Sustainability (Switzerland) 15 (2), https://doi.org/10.3390/su15021690.

32.

Miller

H. J.

Han

, eds. 2009. Geographic Data Mining and Knowledge Discovery (2nd ed.). Boca Raton, FL: CRC Press.

33.

Millington

Luz

. 2021. “Analysis and Classification of Word co-Occurrence Networks from Alzheimer’s Patients and Controls.” Frontiers in Computer Science 3, https://doi.org/10.3389/fcomp.2021.649508.

34.

Moher

Liberati

Tetzlaff

Altman

Group

PRISMA

. 2009. “Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement.” PLoS Medicine 6 (7): e1000097. https://doi.org/10.1371/journal.pmed.1000097 .

35.

Noursalehi

Koutsopoulos

H. N.

Zhao

. 2020. “Machine-Learning-Augmented Analysis of Textual Data: Application in Transit Disruption Management.” IEEE Open Journal of Intelligent Transportation Systems 1: 227–36. https://doi.org/10.1109/OJITS.2020.3038395.

36.

Osorio-Arjona

Horak

Svoboda

Garcia-Ruiz

. 2021. “Social media Semantic Perceptions on Madrid Metro System: Using Twitter Data to Link Complaints to Space.” Sustainable Cities and Society 64, https://doi.org/10.1016/j.scs.2020.102530.

37.

Park

S. D.

2022. “Policy Discourse Among the Chinese Public on Initiatives for Cultural and Creative Industries: Text Mining Analysis.” SAGE Open 12 (1), https://doi.org/10.1177/21582440221079927.

38.

Duffield

, and G, M. A. H. A. 2022. “Rapid Damage Estimation of Texas Winter Storm Uri from Social Media Using Deep Neural Networks.” Urban Science 6 (3), https://doi.org/10.3390/urbansci6030062.

39.

Qin

. 2022. “Identifying Urban Functional Zones by Capturing Multi-Spatial Distribution Patterns of Points of Interest.” International Journal of Digital Earth 15 (1): 2468–94. https://doi.org/10.1080/17538947.2022.2160841.

40.

Rath

Chow

J. Y. J.

. 2022. “Worldwide City Transport Typology Prediction with Sentence-BERT Based Supervised Learning via Wikipedia.” Transportation Research Part C: Emerging Technologies 139, https://doi.org/10.1016/j.trc.2022.103661.

41.

Sander

Ester

Kriegel

H.-P.

. 1998. “Density-based Clustering in Spatial Databases: The Algorithm GDBSCAN and its Applications.” Data Mining and Knowledge Discovery 2: 169–94. https://doi.org/10.1023/A:1009745219419.

42.

Shen

2018. “A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists.” Water Resources Research 54 (11): 8558–93. https://doi.org/10.1029/2018WR022643.

43.

Sung

Chen

Park

S. D.

. 2024. “Who Drives Policy Discourse of China’s Energy Transition: Considering Time Series Perspective, Network and Core-Peripheral Analysis.” SAGE Open 14 (2), https://doi.org/10.1177/21582440241240862.

44.

Szmelter-Jarosz

Rześny-Cieplińska

. 2020. “Priorities of Urban Transport System Stakeholders According to Crowd Logistics Solutions in City Areas. A Sustainability Perspective.” Sustainability (Switzerland) 12 (1), https://doi.org/10.3390/su12010317.

45.

Thakuriah

Tilahun

Zellner

(Eds.) . 2017. Seeing Cities Through Big Data: Research, Methods and Applications in Urban Informatics. Cham, Switzerland: Springer.

46.

Umadevi

Marseline

K. S. J.

. 2017. “A survey on data mining classification algorithms." In 2017 International Conference on Signal Processing and Communication (ICSPC), Coimbatore, India, 2017, pp. 264–68. Piscataway, NJ: IEEE. https://doi.org/10.1109/CSPC.2017.8305851.

47.

Vaz

Arsanjani

J. J.

. 2015. “Predicting Urban Growth of the Greater Toronto Area—Coupling a Markov Cellular Automata with Document Meta-Analysis.” Journal of Environmental Informatics 25 (2): 71–80. https://doi.org/10.3808/jei.201500299.

48.

Wang

W.-C.

Lin

C.-H.

. 2024. “Exploring the Importance of Destination Attributes of Sustainable Urban Waterfronts: Text and Data Mining of Tourists’.” Online Reviews. Sustainability (Switzerland) 16 (6), https://doi.org/10.3390/su16062271.

49.

Wang

Zhang

. 2023. “Deep NMF Topic Modeling.” Neurocomputing 515: 157–73. https://doi.org/10.1016/j.neucom.2022.10.002.

50.

Wang

Zhong

. 2020. “What Were Residents’ Petitions in Beijing-Based on Text Mining.” Journal of Urban Management 9 (2): 228–37. https://doi.org/10.1016/j.jum.2019.11.006.

51.

Weng

M.-H.

Dyer

. 2021. “Ai Augmented Approach to Identify Shared Ideas from Large Format Public Consultation.” Sustainability (Switzerland) 13 (16), https://doi.org/10.3390/su13169310.

52.

Yagoub

M. M.

Alsereidi

A. A.

Mohamed

E. A.

Periyasamy

Alameri

Aldarmaki

Alhashmi

. 2020. “Newspapers as a Validation Proxy for GIS Modeling in Fujairah, United Arab Emirates: Identifying Flood-Prone Areas.” Natural Hazards 104 (1): 111–41. https://doi.org/10.1007/s11069-020-04161-y.

53.

Zheng

Capra

Wolfson

Yang

. 2014. “Urban Computing: Concepts, Methodologies, and Applications.” ACM Transactions on Intelligent Systems and Technology 5 (3): 1–55. https://doi.org/10.1145/2629592.

54.

Zhi

Meng

Wang

Chen

Tian

Yang

Wang

Liu

. 2021. “Spatial Analysis of Urban Residential Sensitivity to Heatwave Events: Case Studies in Five Megacities in China.” Remote Sensing 13 (20), https://doi.org/10.3390/rs13204086.

55.

Zhou

Persello

Stein

. 2023. “Building Use and Mixed-use Classification with a Transformer-Based Network Fusing Satellite Images and Geospatial Textual Information.” Remote Sensing of Environment 297, https://doi.org/10.1016/j.rse.2023.113767.

56.

Zhou

Zou

Mostafavi

Lin

Yang

Gharaibeh

Cai

Abedin

Mandal

. 2022. “VictimFinder: Harvesting Rescue Requests in Disaster Response from Social Media with BERT.” Computers, Environment and Urban Systems 95, https://doi.org/10.1016/j.compenvurbsys.2022.101824.

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