Data-driven urban management: Mapping the landscape

Engin, Zeynep; Dijk, Justin van; Ling, Tian; Longley, Paul; Treleaven, Philip; Batty, Michael; Penn, Alan

doi:10.1016/j.jum.2019.12.001

Cited by 91 publications

(68 citation statements)

References 47 publications

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“…These approaches have the advantage of rapidly identifying generalisable patterns within data, but they can face a challenge from inconsistent definitions or understandings of settlement types (Lilford et al, 2019). Our approach to classification is also data-driven, clustering morphological patterns, though rather than emphasising the generalisability of the classifications, we feel it can be best used as part of "complementary technologies" (Engin et al, 2019) with local stakeholder knowledge and guided by an application. Improvements in image processing are now enabling complete and spatially detailed maps of building footprints to be extracted from VHR imagery for large areas.…”

Section: Discussionmentioning

confidence: 99%

Classifying settlement types from multi-scale spatial patterns of building footprints

Jochem

Leasure

Pannell

et al. 2020

Environment and Planning B: Urban Analytics and City Science

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Urban settlements and urbanised populations continue to grow rapidly and much of this transition is occurring in less developed countries. Remote sensing techniques are now often applied to monitor urbanisation and changes in settlement patterns. In particular, increasing availability of very high resolution imagery (<1 m spatial resolution) and computing power is enabling complete sets of settlement data in the form of building footprints to be extracted from imagery. These settlement data provide information on the changes occurring in cities, particularly in countries which may lack other data on urbanisation. While spatially detailed, extracted building footprints typically lack other information that identify building types or can be used to differentiate intra-urban land uses or neighbourhood types. This work demonstrates an approach to classifying settlement types through multi-scale spatial patterns of urban morphology visible in building footprint data extracted from very high resolution imagery. The work uses a Gaussian mixture modelling approach to select and hierarchically merge components into clusters. The results are maps classifying settlement types on a high spatial resolution (100 m) grid. The approach is applied in Kaduna, Nigeria; Kinshasa, Democratic Republic of the Congo; and Maputo, Mozambique and demonstrates the potential of computational methods to take advantage of large spatial datasets and extract meaningful information to support monitoring of urban areas. The model-based approach produces a hierarchy of potential clustering solutions, and we suggest that this can be used in partnership with local knowledge of the context when creating settlement typologies.

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Section: Discussionmentioning

confidence: 99%

Classifying settlement types from multi-scale spatial patterns of building footprints

Jochem

Leasure

Pannell

et al. 2020

Environment and Planning B: Urban Analytics and City Science

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“…Specifically, the incorrect transmission of urban data does not only require time, but also retransmits the information flow, which negatively affects the quality of the data [168]. As a result, the quality of data and the technologies to overcome problems of inconsistency, partiality, and unreliability should be considered in the development and implementation of sensor city projects [169,170].…”

Section: Discussionmentioning

confidence: 99%

Understanding Sensor Cities: Insights from Technology Giant Company Driven Smart Urbanism Practices

D’Amico

L’Abbate

Liao

et al. 2020

Sensors

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The data-driven approach to sustainable urban development is becoming increasingly popular among the cities across the world. This is due to cities’ attention in supporting smart and sustainable urbanism practices. In an era of digitalization of urban services and processes, which is upon us, platform urbanism is becoming a fundamental tool to support smart urban governance, and helping in the formation of a new version of cities—i.e., City 4.0. This new version utilizes urban dashboards and platforms in its operations and management tasks of its complex urban metabolism. These intelligent systems help in maintaining the robustness of our cities, integrating various sensors (e.g., internet-of-things) and big data analysis technologies (e.g., artificial intelligence) with the aim of optimizing urban infrastructures and services (e.g., water, waste, energy), and turning the urban system into a smart one. The study generates insights from the sensor city best practices by placing some of renowned projects, implemented by Huawei, Cisco, Google, Ericsson, Microsoft, and Alibaba, under the microscope. The investigation findings reveal that the sensor city approach: (a) Has the potential to increase the smartness and sustainability level of cities; (b) Manages to engage citizens and companies in the process of planning, monitoring and analyzing urban processes; (c) Raises awareness on the local environmental, social and economic issues, and; (d) Provides a novel city blueprint for urban administrators, managers and planners. Nonetheless, the use of advanced technologies—e.g., real-time monitoring stations, cloud computing, surveillance cameras—poses a multitude of challenges related to: (a) Quality of the data used; (b) Level of protection of traditional and cybernetic urban security; (c) Necessary integration between the various urban infrastructure, and; (d) Ability to transform feedback from stakeholders into innovative urban policies.

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“…Primarily concerned with the development and management of the built environment, the urban planning community has been paying close attention to smart cities. Engin et al [25] discuss the impact of digital technology on three levels of urban management, namely, framing the future, evidence-based planning decisions, and real-time management. Framing the future refers to understanding and envisioning how new technologies, such as artificial intelligence, blockchains, platform technologies, and virtual/augmented reality, can disrupt existing models and redefine how cities are run in the future.…”

Section: Urban Planningmentioning

confidence: 99%

“…Evidence-based planning decisions refer to the use of empirical evidence, generated with the aid of digital data, to inform and scrutinize public decisions on infrastructure investment and land development. Engin et al [25] suggest that an analytics-enabled planning could serve as an objective approach that helps overcome issues such as NIMBYism, where residents often have the tendency to block developments or projects affecting their neighborhoods. The goal in this line of work is to develop a data-driven "evidence base" for planning decision support.…”

Section: Urban Planningmentioning

confidence: 99%

Enabling Smarter Cities with Operations Management

Mak

2022

M&SOM

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Recent development of new technologies and data analytics has unleashed the so-called smart city (SC) movement, which has been transforming the ways that cities (and their services) operate. This movement is in line with the operations management (OM) community’s pursuit of innovative research questions, and part of our community has already started working on this exciting area of SC OM. In this article, we shall discuss (i) the potential contribution that OM can make to the SC movement, that is, how OM research may help inform discussion and decision making in the public and private sectors. In particular, the core principle of making cities smarter is in line with the OM literature. We then discuss (ii) how and why SC operations can be an interesting topic that possibly expands the scope and depth of OM theory, through drawing links to the existing and new streams of OM literature. Finally, we present (iii) a few promising research directions and examples, in domains such as energy, transportation (mobility), and retail.

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Data-driven urban management: Mapping the landscape

Cited by 91 publications

References 47 publications

Classifying settlement types from multi-scale spatial patterns of building footprints

Classifying settlement types from multi-scale spatial patterns of building footprints

Understanding Sensor Cities: Insights from Technology Giant Company Driven Smart Urbanism Practices

Enabling Smarter Cities with Operations Management

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