daftarPustaka.bib

@article{Allam2019,
   abstract = {Cities are increasingly turning towards specialized technologies to address issues related to society, ecology, morphology and many others. The emerging concept of Smart Cities highly encourages this prospect by promoting the incorporation of sensors and Big Data through the Internet of Things (IoT). This surge of data brings new possibilities in the design and management of cities just as much as economic prospects. While Big Data processing through Artificial Intelligence (AI) can greatly contribute to the urban fabric, sustainability and liveability dimensions however must not be overlooked in favour of technological ones. This paper reviews the urban potential of AI and proposes a new framework binding AI technology and cities while ensuring the integration of key dimensions of Culture, Metabolism and Governance; which are known to be primordial in the successful integration of Smart Cities for the compliance to the Sustainable Development Goal 11 and the New Urban Agenda. This paper is aimed towards Policy Makers, Data Scientists and Engineers who are looking at enhancing the integration of Artificial Intelligence and Big Data in Smart Cities with an aim to increase the liveability of the urban fabric while boosting economic growth and opportunities.},
   author = {Zaheer Allam and Zaynah A. Dhunny},
   doi = {10.1016/j.cities.2019.01.032},
   issn = {02642751},
   journal = {Cities},
   keywords = {Artificial intelligence,Big data,Internet of things,Liveability,Smart cities,Sustainability},
   month = {6},
   pages = {80-91},
   publisher = {Elsevier Ltd},
   title = {On big data, artificial intelligence and smart cities},
   volume = {89},
   year = {2019},
}
@article{Allam2018,
   abstract = {The Smart City concept is still evolving and can be viewed as a branding exercise by big corporations, which is why the concept is not being used by the United Nations (U.N.). Smart Cities tend to represent the information, communication, and technological (ICT) industry alone without considering the values and cultural and historical profiles that some cities hold as legacies. However, the technology inherent in Smart Cities promises efficiencies and options that could allow cities to be more “inclusive, safe, resilient, and sustainable” as required by the U.N. agenda including cultural heritage. There is a notable lack of Smart City application to cultural and historical urban fabrics. Instead, the modernist new town approach has emerged under this new rubric leading to many problems such as urban decay and unsustainable car dependence. This study therefore presents a review of the literature on the nature, challenges, and opportunities of Smart Cities. A new Smart Cities framework is proposed based on the dimensions of culture, metabolism, and governance. These findings seek to inform policy makers of an alternative viewpoint on the Smart City paradigm, which focuses on urban outcomes rather than technology in isolation.},
   author = {Zaheer Allam and Peter Newman},
   doi = {10.3390/smartcities1010002},
   issn = {26246511},
   issue = {1},
   journal = {Smart Cities},
   keywords = {Culture,Governance,Metabolism,Smart cities},
   month = {12},
   pages = {4-25},
   publisher = {MDPI},
   title = {Redefining the smart city: Culture, metabolism and governance},
   volume = {1},
   year = {2018},
}
@article{Bjola2022,
   abstract = {The arrival of AI technology promises to add a fascinating new chapter to development theory and practice. Current studies have made good progress in examining the potential contributions of AI to achieving sustainable development goals and addressing challenges in specific development areas (poverty, global health, human rights, environment etc.). However, four lessons stand out when considering the impact of future research on the AI/development nexus: learning how to access and combine data from multiple sources, how to master AI techniques to extract analytical insight, how to build socially impactful AI solutions, and how to apply AI to development in an ethically responsible fashion. This paper makes the argument that AI could radically transform development theory and practice by prompting a rethinking of how data and algorithms come together to generate insights into the way in which development challenges are identified, studied, and managed.},
   author = {Corneliu Bjola},
   doi = {10.1080/13600818.2021.1960960},
   issn = {14699966},
   issue = {1},
   journal = {Oxford Development Studies},
   keywords = {AI4SG,Artificial Intelligence,SDGs,machine learning},
   pages = {78-90},
   publisher = {Routledge},
   title = {AI for development: implications for theory and practice},
   volume = {50},
   year = {2022},
}
@article{Brinkley2021,
   abstract = {Land-use control is local and highly varied. State agencies struggle to assess plan contents. Similarly, advocacy groups and planning researchers wrestle with the length of planning documents and ability to compare across plans. The goal of this research is to (1) introduce Natural Language Processing techniques that can automate qualitative coding in planning research and (2) provide policy-relevant exploratory findings. We assembled a database of 461 California city-level General Plans, extracted the text, and used topic modeling to identify areas of emphasis (clusters of co-occurring words). We find that California city general plans address more than sixty topics, including greenhouse gas mitigation and Climate Action Planning. Through spatializing results, we find that a quarter of the topics in plans are regionally specific. We also quantify the rift and convergence of planning topics. The topics focused on housing have very little overlap with other planning topics. This is likely a factor of state requirements to update and evolve the Housing Elements every five years, but not other aspects of General Plans. This finding has policy implications as housing topics evolve away from other emphasis areas such as transportation and economic development. Furthermore, the topic modeling approach reveals that many cities have had a focus on environmental justice through Health and Wellness Elements well before the state mandate in 2019. Our searchable state-level database of general plans is the first for California—and nationally. We provide a model for others that wish to comprehensively assess and compare plan contents using machine learning.},
   author = {Catherine Brinkley and Carl Stahmer},
   doi = {10.1177/0739456X21995890},
   issn = {0739456X},
   journal = {Journal of Planning Education and Research},
   keywords = {California,General Plan,computational linguistics,machine learning},
   publisher = {SAGE Publications Inc.},
   title = {What Is in a Plan? Using Natural Language Processing to Read 461 California City General Plans},
   year = {2021},
}
@article{Cai2021,
   abstract = {Natural language processing (NLP) has shown potential as a promising tool to exploit under-utilized urban data sources. This paper presents a systematic review of urban studies published in peer-reviewed journals and conference proceedings that adopted NLP. The review suggests that the application of NLP in studying cities is still in its infancy. Current applications fell into five areas: urban governance and management, public health, land use and functional zones, mobility, and urban design. NLP demonstrates the advantages of improving the usability of urban big data sources, expanding study scales, and reducing research costs. On the other hand, to take advantage of NLP, urban researchers face challenges of raising good research questions, overcoming data incompleteness, inaccessibility, and non-representativeness, immature NLP techniques, and computational skill requirements. This review is among the first efforts intended to provide an overview of existing applications and challenges for advancing urban research through the adoption of NLP.},
   author = {Meng Cai},
   doi = {10.1016/j.heliyon.2021.e06322},
   issn = {24058440},
   issue = {3},
   journal = {Heliyon},
   keywords = {Natural language processing,Text mining,Urban big data,Urban research},
   month = {3},
   publisher = {Elsevier Ltd},
   title = {Natural language processing for urban research: A systematic review},
   volume = {7},
   year = {2021},
}
@article{Fu2023,
   abstract = {Problem, research strategy, and findings: Planners need to read plans to learn and adapt current practice. Planners may struggle to find time to read and study lengthy planning documents, especially in emerging areas such as climate change and urban resilience. Recently, natural language processing (NLP) has shown promise in processing big textual data. We asked whether planners could use NLP techniques to more efficiently extract useful and reliable information from planning documents. By analyzing 78 resilience plans from the 100 Resilient Cities Network, we found that results generated from topic modeling, which is an NLP technique, coincided to a large extent (80%) with those from the conventional content analysis approach. Topic modeling was generally effective and efficient in extracting the main information of plans, whereas the content analysis approach could find more in-depth details but at the expense of considerable time and effort. We further propose a transferrable model for cutting-edge planners to more efficiently read and study a large collection of plans using machine learning. Our methodology has limitations: Both topic modeling and content analysis can be subject to human bias and generate unreliable results; NLP text processing techniques may create inaccurate results due to their specific method limitations; and the transferable approach can be only applied to big textual data where there are enough sufficiently long documents. Takeaway for practice: NLP represents a valuable addition to the planner’s toolbox. Topic modeling coupled with other NLP techniques can help planners to effectively discover key topics in plans, identify planning priorities and plans of specific emphasis, and find relevant policies.},
   author = {Xinyu Fu and Chaosu Li and Wei Zhai},
   doi = {10.1080/01944363.2022.2038659},
   issn = {01944363},
   issue = {1},
   journal = {Journal of the American Planning Association},
   keywords = {machine learning,natural language processing,plan evaluation,urban resilience},
   pages = {107-119},
   publisher = {Routledge},
   title = {Using Natural Language Processing to Read Plans: A Study of 78 Resilience Plans From the 100 Resilient Cities Network},
   volume = {89},
   year = {2023},
}
@article{Hasler2017,
   abstract = {Rapid urbanization, climate change, resource depletion, the desire for more sustainable development and widespread use of the Internet and mobile phones are major challenges for urban planning. While the smart city model is seen as a means to cope with these challenges, it is often reduced to an amalgam of technologies. Citizens are usually seldom included in the planning process, though the knowledge they produce and can share on how they use and live in the city is extremely valuable. Digital technologies create an opportunity to reshape the planning process by improving interactions and information exchanges among urban planners and citizens, which are central in the move towards more sustainable, responsive planning. This research aims to answer the following two questions: (1) how is digital participation changing the role citizens play in urban planning and decision making processes? and, (2) what are the advantages and limitations of involving citizens in these processes through digital tools? This paper explores how digital tools can be harnessed to enhance citizen involvement in the planning process. We will give an overview of how these tools can inform urban planning by providing citizen-centric data to foster more inclusive and responsive planning. This paper identifies both the opportunities - particularly in terms of data production and exchange – and limitations of digital tools.},
   author = {Stéphanie Hasler and Jérôme Chenal and Marc Soutter},
   doi = {10.13189/cea.2017.050605},
   issn = {2332-1091},
   issue = {6},
   journal = {Civil Engineering and Architecture},
   month = {12},
   pages = {230-239},
   publisher = {Horizon Research Publishing Co., Ltd.},
   title = {Digital Tools as a Means to Foster Inclusive, Data-informed Urban Planning},
   volume = {5},
   year = {2017},
}
@article{Herath2022,
   abstract = {Recently, the population density in cities has increased at a higher pace. According to the United Nations Population Fund, cities accommodated 3.3 billion people (54%) of the global population in 2014. By 2050, around 5 billion people (68%) will be residing in cities. In order to make lifestyles in cities more comfortable and cost-effective, the city must be smart and intelligent. It is mainly accomplished through an intelligent decision-making process using computational intelligence-based technologies. This paper explored how artificial intelligence (AI) is being used in the smart city concept. From 2014 to 2021, we examined 133 articles (97% of Scopus and 73% of WoS) in healthcare, education, environment and waste management, agriculture, mobility and smart transportation, risk management, and security. Moreover, we observed that the healthcare (23% impact), mobility (19% impact), privacy and security (11% impact), and energy sectors (10% impact) have a more significant influence on AI adoption in smart cities. Since the epidemic hit cities in 2019, the healthcare industry has intensified its AI-based advances by 60%. According to the analysis, AI algorithms such as ANN, RNN/LSTM, CNN/R-CNN, DNN, and SVM/LS-SVM have a higher impact on the various smart city domains.},
   author = {H. M.K.K.M.B. Herath and Mamta Mittal},
   doi = {10.1016/j.jjimei.2022.100076},
   issn = {26670968},
   issue = {1},
   journal = {International Journal of Information Management Data Insights},
   keywords = {Artificial intelligence (AI),Digital cities,Intelligent interaction,Internet of Things (IoT),Smart cities},
   month = {4},
   publisher = {Elsevier B.V.},
   title = {Adoption of artificial intelligence in smart cities: A comprehensive review},
   volume = {2},
   year = {2022},
}
@article{Guardia2021,
   abstract = {The evidence-based policy movement promotes the use of empirical evidence to inform policy decision-making. While several social science disciplines are undergoing a ‘credibility revolution’ focused on openness and replication, policy analysis has yet to systematically embrace transparency and reproducibility. We argue that policy analysis should adopt the open research practices increasingly espoused in related disciplines to advance the credibility of evidence-based policy making. We first discuss the importance of evidence-based policy in an era of increasing disagreement about facts, analysis, and expertise. We present a novel framework for ‘open’ policy analysis (OPA) and how to achieve it, focusing on examples of recent policy analyses that have incorporated open research practices such as transparent reporting, open data, and code sharing. We conclude with recommendations on how key stakeholders in evidence-based policy can make OPA the norm and thus safeguard trust in using empirical evidence to inform important public policy decisions.},
   author = {Fernando Hoces de la Guardia and Sean Grant and Edward Miguel},
   doi = {10.1093/scipol/scaa067},
   issn = {0302-3427},
   issue = {2},
   journal = {Science and Public Policy},
   month = {4},
   pages = {154-163},
   title = {A framework for open policy analysis},
   volume = {48},
   url = {https://doi.org/10.1093/scipol/scaa067},
   year = {2021},
}
@article{Kaczmarek2022,
   abstract = {Spatial development plans are the basic tool for shaping spatial policy and have an impact on the implementation of the concept of sustainable development. Monitoring the implementation of plans can be difficult where no standard of plans exists that allows for obtaining comprehensive information on the arrangements of the plans, including future land development. The purpose of the research is to integrate spatial development plans by analyzing and classifying their textual content. We use machine learning methods for the processing of the text of plans and their classification. The result is a model, that classifies the texts of findings for individual areas in the plan into defined land use categories. We use machine learning methods in natural language processing for the analyzing of the text part of plans and their classification. The results indicate the best quality of the model when using neural networks. The proposed approach allows for obtaining comprehensive information on the planned land use of the area, derived from many heterogeneous planning documents. Due to the combination of textual arrangements with spatial data, it allows both for the unification of land use classification and then integration of multiple spatial development plans in spatial dimension.},
   author = {Iwona Kaczmarek and Adam Iwaniak and Aleksandra Świetlicka and Mateusz Piwowarczyk and Adam Nadolny},
   doi = {10.1016/j.scs.2021.103479},
   issn = {22106707},
   journal = {Sustainable Cities and Society},
   keywords = {GRU,LSTM,Neural networks,Spatial development plan,Spatial planning,Text classification,Unsupervised machine learning},
   month = {1},
   publisher = {Elsevier Ltd},
   title = {A machine learning approach for integration of spatial development plans based on natural language processing},
   volume = {76},
   year = {2022},
}
@article{Kandt2021,
   abstract = {The analysis of big data is deemed to define a new era in urban research, planning and policy. Real-time data mining and pattern detection in high-frequency data can now be carried out at a large scale. Novel analytical practices promise smoother decision-making as part of a more evidence-based and smarter urbanism, while critical voices highlight the dangers and pitfalls of instrumental, data-driven city making to urban governance. Less attention has been devoted to identifying the practical conditions under which big data can realistically contribute to addressing urban policy problems. In this paper, we discuss the value and limitations of big data for long-term urban policy and planning. We first develop a theoretical perspective on urban analytics as a practice that is part of a new smart urbanism. We identify the particular tension of opposed temporalities of high-frequency data and the long durée of structural challenges facing cities. Drawing on empirical studies using big urban data, we highlight epistemological and practical challenges that arise from the analysis of high-frequency data for strategic purposesand formulate propositions on the ways in which urban analytics can inform long-term urban policy.},
   author = {Jens Kandt and Michael Batty},
   doi = {10.1016/j.cities.2020.102992},
   issn = {02642751},
   journal = {Cities},
   keywords = {Big data,Mobilities,Smart cities,Urban analytics,Urban policy},
   month = {2},
   publisher = {Elsevier Ltd},
   title = {Smart cities, big data and urban policy: Towards urban analytics for the long run},
   volume = {109},
   year = {2021},
}
@inproceedings{Puron-Cid2012,
   abstract = {For a long time, governments have promoted initiatives to make a great diversity of information available in order to enhance productivity, effectiveness and strategic decision-making. Today, a revitalized wave of open access to data has focused on making government activities more transparent, participatory and collaborative; together, these activities represent "open government." Some open data initiatives are intensively supported by the use of flexible and powerful information technologies and various analytical methods. This paper argues that there is a new window of opportunity to combine emergent information technologies, sophisticated analytical methods, and a great diversity of datasets in order to improve government capabilities and make better decisions. However, this strategy, which we are now calling IT-enabled policy analysis, would require adequate governance models, individuals with analytical skills, the availability of adequate data, and sophisticated information technologies. The potential benefits of creating organizations with powerful analytical capabilities within governments, universities, and non-government organizations are numerous and the impact on society could be great. However, there are also some important political, organizational, and technical challenges. © 2012 ACM.},
   author = {Gabriel Puron-Cid and J. Ramon Gil-Garcia and Luis F. Luna-Reyes},
   doi = {10.1145/2307729.2307746},
   isbn = {9781450314039},
   journal = {ACM International Conference Proceeding Series},
   keywords = {open data,policy analysis,policy informatics,technology tools},
   pages = {97-106},
   title = {IT-enabled policy analysis: New technologies, sophisticated analysis and open data for better government decisions},
   year = {2012},
}
@article{Sanchez2022,
   abstract = {Over the past several decades, urban planning has considered a variety of advanced analysis methods with greater and lesser degrees of adoption. Geographic Information Systems (GIS) is probably the most notable, with others such as database management systems (DBMS), decision support systems (DSS), planning support systems (PSS), and expert systems (ES), having mixed levels of recognition and acceptance (Kontokosta, C. E. (2021). Urban informatics in the science and practice of planning. Journal of Planning Education and Research, 41(4), 382–395. doi:10.1177/0739456X18793716; Yigitcanlar, T., Desouza, K. C., Butler, L., & Roozkhosh, F. (2020). Contributions and risks of artificial intelligence (AI) in building smarter cities: Insights from a systematic review of the literature. Energies, 13(6), 1473). Advances in information technologies have moved very slowly in the field of urban planning, more recently concerning ‘smart city’ technologies while revolutionizing other domains, such as consumer goods and services. Baidu, Amazon, Netflix, Google, and many others are using these technologies to gain insights into consumer behaviour and characteristics and improve supply chains and logistics. This is an opportune time for urban planners to consider the application of AI-related techniques given vast increases in data availability, increased processing speeds, and increased popularity and development of planning related applications. Research on these topics by urban planning scholars has increased over the past few years, but there is little evidence to suggest that the results are making it into the hands of professional planners (Batty, M. (2018). Artificial intelligence and smart cities. Environment and Planning B: Urban Analytics and City Science, 45(1), 3–6; Batty, M. (2021). Planning education in the digital age. Environment and Planning B: Urban Analytics and City Science, 48(2), 207–211). Others encourage planners to leverage the ubiquity of data and advances in computing to enhance redistributive justice in information resources and procedural justice in decision-making among marginalized communities (Boeing, G., Besbris, M., Schachter, A., & Kuk, J. (2020). Housing search in the Age of Big data: Smarter cities or the same Old blind spots? Housing Policy Debate, 31(1), 112–126; Goodspeed, R. (2015). Smart cities: Moving beyond urban cybernetics to tackle wicked problems. Cambridge journal of regions, Economy and Society, 8(1), 79–92). This article highlights findings from a recent literature review on AI in planning and discusses the results of a national survey of urban planners about their perspectives on AI adoption and concerns they have expressed about its broader use in the profession. Currently, the outlook is mixed, matching how urban planners initially viewed the early stages of computer adoption within the profession. And yet today, personal computers are essential to any job.},
   author = {Thomas W. Sanchez and Hannah Shumway and Trey Gordner and Theo Lim},
   doi = {10.1080/12265934.2022.2102538},
   issn = {21616779},
   journal = {International Journal of Urban Sciences},
   keywords = {Urban planning,artificial intelligence,data science,technology},
   publisher = {Routledge},
   title = {The prospects of artificial intelligence in urban planning},
   year = {2022},
}