Processes, Potential Benefits, and Limitations of Big Data Analytics: A Case Analysis of 311 Data from City of Miami

Hagen, Loni; Yi, Hye Seon; Pietri, Siana; Keller, Thomas E.

doi:10.1145/3325112.3325212

Cited by 8 publications

(11 citation statements)

References 14 publications

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“…First, new emerging data sources should be utilized to discover more insightful patterns. In existing works, the most widely used data sources have been 311 service‐request data (Hagen, Seon Yi, Pietri, Keller, & T., 2019) and social media data (Ghodousi et al., 2019; Wang, Qian, Kats, Kontokosta, & Sobolevsky, 2017), which are almost subjectively reported by users and belong to voluntary geographic information (VGI) data (Elwood, Goodchild, & Sui, 2012). These data sets may be sparse and missing spatial and temporal information.…”

Section: Introductionmentioning

confidence: 99%

“…Spatiotemporal visualization technology is an efficient and lightweight method that has been widely used in many research fields, such as criminology (Nakaya & Yano, 2010) and urban transport (Kang, Cho, & Son, 2018). This technology can provide comprehensive and valuable information from data sources and predict patterns in citizen requirements and mobility, which will help governments understand urban issues and allocate resources efficiently (Hagen et al., 2019). Although many analysis methods, such as kernel density estimation (KDE; Ye, Xu, Lee, Zhu, & Wu, 2015) and k ‐means clustering (Hagen et al., 2019), have been widely used to study urban issues, they often focus on two‐dimensional visualization instead of three‐dimensional techniques.…”

Section: Introductionmentioning

confidence: 99%

“…This technology can provide comprehensive and valuable information from data sources and predict patterns in citizen requirements and mobility, which will help governments understand urban issues and allocate resources efficiently (Hagen et al., 2019). Although many analysis methods, such as kernel density estimation (KDE; Ye, Xu, Lee, Zhu, & Wu, 2015) and k ‐means clustering (Hagen et al., 2019), have been widely used to study urban issues, they often focus on two‐dimensional visualization instead of three‐dimensional techniques. To explore time variations, three‐dimensional methods, such as space‐time cubes, provide an opportunity to address this shortcoming (Andrienko et al., 2010).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Visualizing spatiotemporal patterns of city service demand through a space‐time exploratory approach

Jing

Zhu

et al. 2021

Transactions in GIS

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City service demand fluctuates across space and time. Although various data, such as 311 hotline data and social media data, have been used to explore the spatiotemporal patterns of city services, data uncertainty and the uneven distribution of service demand are overlooked to some extent and thus could result in bias. To overcome these shortcomings, top‐down collected city service data that fully cover urban areas are used as an emerging data source in this article. A visual analytical approach that employs a three‐dimensional model based on a space‐time cube combined with the Mann–Kendall algorithm is developed and applied in Xicheng District, Beijing, China. The results show that in comparison to other methods, the emerging data and visualization methods have more power to explain city services in terms of overall trends and micro‐scale details. For instance, city service cases demonstrate a significant downward trend. Meanwhile, the distribution of hotspots/coldspots is found to be related to the built environment and population density. For example, high‐incidence cases are located in some communities that are the key governance areas, indicating a demand to increase the staffing of grid administrators. The findings of this work can potentially benefit other cities in China and worldwide.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Visualizing spatiotemporal patterns of city service demand through a space‐time exploratory approach

Jing

Zhu

et al. 2021

Transactions in GIS

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“…For examples, clustering algorithms are used to organize the citizen's service needs to groups. A couple of patterns recognized by these techniques provide perspectives that would not be evident using traditional approaches like manual scanning [11].…”

Section: Introductionmentioning

confidence: 99%

“…They investigated to what extent 311 request patterns can reveal socio-demographic structures. As a result, they found that 311 service request patterns indicate underlying socio-demographic factors within the area [11]. Liu et al, used a factor analysis method for identifying different requests.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Citizen Satisfaction and Prioritizing Their Needs Based on Citizens’ Complaint Data

et al. 2019

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Citizen Relationship Management (CiRM) is one of the important matters in citizen-centric e-government. In fact, the most important purpose of e-government is to satisfy citizens. The ‘137 system’ is one of the most important ones based on the citizen-centric that is a municipality phone based request/response system. The aim of this research is a data-mining of a ‘137 system’ (citizens’ complaint system) of the first district of Bojnourd municipality in Iran, to prioritize the urban needs and to estimate citizens’ satisfaction. To reach this, the K-means and Bees Algorithms (BA) were used. Each of these two algorithms was executed using two different methods. In the first method, prioritization and estimation of satisfaction were done separately, whereas in the second method, prioritization and estimation of satisfaction were done simultaneously. To compare the clustering results in the two methods, an index was presented quantitatively. The results showed the superiority of the second method. The index of the second method for the first needs in K-means was 0.299 more than the first method and it was the same in two methods in BA. Also, the results of the BA clustering were better at it because of the S (silhouette) and CH (Calinski-Harabasz) indexes. Considering the final prioritization done by the two algorithms in two methods, the primary needs included asphalt, so specific schemes should be considered.

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Big data, machine learning and uncertainty in foresight studies

Muraro,

Salles-Filho

2024

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Purpose Currently, foresight studies have been adapted to incorporate new techniques based on big data and machine learning (BDML), which has led to new approaches and conceptual changes regarding uncertainty and how to prospect future. The purpose of this study is to explore the effects of BDML on foresight practice and on conceptual changes in uncertainty. Design/methodology/approach The methodology is twofold: a bibliometric analysis of BDML-supported foresight studies collected from Scopus up to 2021 and a survey analysis with 479 foresight experts to gather opinions and expectations from academics and practitioners related to BDML in foresight studies. These approaches provide a comprehensive understanding of the current landscape and future paths of BDML-supported foresight research, using quantitative analysis of literature and qualitative input from experts in the field, and discuss potential theoretical changes related to uncertainty. Findings It is still incipient but increasing the number of prospective studies that use BDML techniques, which are often integrated into traditional foresight methodologies. Although it is expected that BDML will boost data analysis, there are concerns regarding possible biased results. Data literacy will be required from the foresight team to leverage the potential and mitigate risks. The article also discusses the extent to which BDML is expected to affect uncertainty, both theoretically and in foresight practice. Originality/value This study contributes to the conceptual debate on decision-making under uncertainty and raises public understanding on the opportunities and challenges of using BDML for foresight and decision-making.

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Processes, Potential Benefits, and Limitations of Big Data Analytics: A Case Analysis of 311 Data from City of Miami

Cited by 8 publications

References 14 publications

Visualizing spatiotemporal patterns of city service demand through a space‐time exploratory approach

Visualizing spatiotemporal patterns of city service demand through a space‐time exploratory approach

Evaluating Citizen Satisfaction and Prioritizing Their Needs Based on Citizens’ Complaint Data

Big data, machine learning and uncertainty in foresight studies

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