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Mining Social Media and Structured Data in Urban Environmental Management to Develop Smart Cities

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Title: Mining Social Media and Structured Data in Urban Environmental Management to Develop Smart Cities
Authors: Du, Xu
Summary: This research presented the deployment of data mining on social media and structured data in urban studies. We analyzed urban relocation, air quality and traffic parameters on multicity data as early work. We applied the data mining techniques of association rules, clustering and classification on urban legislative history. Results showed that data mining could produce meaningful knowledge to support urban management. We treated ordinances (local laws) and the tweets about them as indicators to assess urban policy and public opinion. Hence, we conducted ordinance and tweet mining including sentiment analysis of tweets. This part of the study focused on NYC with a goal of assessing how well it heads towards a smart city. We built domain-specific knowledge bases according to widely accepted smart city characteristics, incorporating commonsense knowledge sources for ordinance-tweet mapping. We developed decision support tools on multiple platforms using the knowledge discovered to guide urban management. Our research is a concrete step in harnessing the power of data mining in urban studies to enhance smart city development.
URL: https://digitalcommons.montclair.edu/etd/697
Database: OpenDissertations
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  Availability: 0
Header DbId: ddu
DbLabel: OpenDissertations
An: ddu.oai.digitalcommons.montclair.edu.etd.1698
AccessLevel: 6
PubType: Dissertation/ Thesis
PubTypeId: dissertation
PreciseRelevancyScore: 0
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  Data: Mining Social Media and Structured Data in Urban Environmental Management to Develop Smart Cities
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– Name: Abstract
  Label: Summary
  Group: Ab
  Data: This research presented the deployment of data mining on social media and structured data in urban studies. We analyzed urban relocation, air quality and traffic parameters on multicity data as early work. We applied the data mining techniques of association rules, clustering and classification on urban legislative history. Results showed that data mining could produce meaningful knowledge to support urban management. We treated ordinances (local laws) and the tweets about them as indicators to assess urban policy and public opinion. Hence, we conducted ordinance and tweet mining including sentiment analysis of tweets. This part of the study focused on NYC with a goal of assessing how well it heads towards a smart city. We built domain-specific knowledge bases according to widely accepted smart city characteristics, incorporating commonsense knowledge sources for ordinance-tweet mapping. We developed decision support tools on multiple platforms using the knowledge discovered to guide urban management. Our research is a concrete step in harnessing the power of data mining in urban studies to enhance smart city development.
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RecordInfo BibRecord:
  BibEntity:
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: data mining
        Type: general
      – SubjectFull: text mining
        Type: general
      – SubjectFull: ordinances
        Type: general
      – SubjectFull: urban policy
        Type: general
      – SubjectFull: sentiment analysis
        Type: general
      – SubjectFull: Environmental Sciences
        Type: general
      – SubjectFull: Urban policy--New York (State)--New York, Cities and towns-- Research, Data mining, Social media--Data processing
        Type: general
    Titles:
      – TitleFull: Mining Social Media and Structured Data in Urban Environmental Management to Develop Smart Cities
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Du, Xu
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2021
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