USING NATURAL LANGUAGE PROCESSING TO DETECT URBAN PROBLEMS FOR GOVERNANCE

The chapters in this book were first presented in a 2-day workshop on Big Data and Urban Informatics held at the University of Illinois at Chicago in 2014. The workshop, sponsored by the National Science Foundation, brought together approximately 150 educators, practitioners and students from 91 different institutions in 11 countries. Participants represented a variety of academic disciplines including Urban Planning, Computer Science, Civil Engineering, Economics, Statistics, and Geography and provided a unique opportunity for discussions by urban social scientists and data scientists interested in the use of Big Data to address urban challenges. The papers in this volume are a selected subset of those presented at the workshop and have gone through a peer-review process.

Singapore’s “smart city” agenda is driving the government to provide public access to a broader variety of urban informatics sources, such as images from traffic cameras and information about buses servicing different bus stops. Such informatics data serves as probes of evolving conditions at different spatiotemporal scales. This paper explores how such multi-modal informatics data can be used to establish the normal operating conditions at different city locations, and then apply appropriate outlier-based analysis techniques to identify anomalous events at these selected locations. We will introduce the overall architecture of sociophysical analytics, where such infrastructural data sources can be combined with social media analytics to not only detect such anomalous events, but also localize and explain them. Using the annual Formula-1 race as our candidate event, we demonstrate a key difference between the discriminative capabilities of different sensing modes: while social media streams provide discriminative signals during or prior to the occurrence of such an event, urban informatics data can often reveal patterns that have higher persistence, including before and after the event. In particular, we shall demonstrate how combining data from (i) publicly available Tweets, (ii) crowd levels aboard buses, and (iii) traffic cameras can help identify the Formula-1 driven anomalies, across different spatiotemporal boundaries.

With the development of natural resources management from extensive management to refined management, the traditional survey methods had problems such as single survey methods and data sources, and less detailed data of urban land space. It was difficult to meet the needs of urban land space monitoring and refined management of natural resources. Therefore, more detailed urban background resource data was urgently needed to support the needs of land space planning and refined management of natural resources. Through the research of multi technology integrated urban background resource investigation method, this paper conducts detailed investigation and integrated application on the production, life, ecology, history and humanities of urban land space, so as to provide better data and technical support for the fine management of natural resources.

Urban-rural integration is an important way to realize coordinated regional development, and the development of digital economy provides a new impetus for the improvement of urban-rural integration efficiency. This paper takes the urban-rural integration efficiency of Guangdong Province as the research object, and measures and analyzes the urban-rural integration efficiency of the region by using the decision-making model based on inputs and outputs (DEA-CCR) and the BCC model with variable returns to scale of production. The results found that the development level of digital economy is significantly and positively correlated with urban-rural integration efficiency, and there are differences in the integration efficiency driven by digital economy in different regions. The dynamic trend is further analyzed through the Malmquist index, and it is found that technological progress and efficiency improvement are the key factors affecting the integration efficiency. Accordingly, the following optimization strategies are proposed: strengthen the construction of rural digital infrastructure to enhance its digital access level; promote the deep integration of digital technology and rural industries to cultivate new business forms of rural digital economy; and promote the free flow and sharing of urban and rural data resources to enable the synergistic development of urban and rural industries.

This synthesis describes statewide travel forecasting models designed to address planning needs and provide forecasts for statewide transportation, including passenger vehicle and freight movement. It discusses the types and purposes of models being used, integration of state and urban models, data requirements, computer needs, resources (including time, funding, training, and staff), limitations, and overall benefits. Five case studies are included, two that focus on passenger components, two on freight components, and one on both passenger and freight. In addition, definitions of common technical terms and an annotated bibliography of statewide and national forecasting techniques are provided. An excerpt from the Guidebook on Statewide Travel Forecasting Models concerning passenger and intercity travel forecasting is included as an appendix. This synthesis effort was based on the results of surveys received from each state that has a statewide travel forecasting model. A literature review was also undertaken, with major information sources (studies, databases, surveys, and programs) cited and discussed.

The exponential growth of urban populations has led to complex challenges in managing infrastructure, energy, transportation, healthcare, and environmental sustainability. Smart cities have emerged as a response to these challenges, integrating advanced technologies such as the Internet of Things (IoT) and Big Data analytics to optimize urban services and resource utilization. This research presents a comprehensive model for developing smart city infrastructure that leverages IoT sensors for real-time data collection and Big Data frameworks for large-scale analytics. The study also includes a simulation-based performance evaluation using defined parameters, analyzing factors such as data latency, system scalability, and resource efficiency. The results demonstrate that the integration of IoT and Big Data can significantly enhance urban functionality, improve operational efficiency, and support sustainable city development.

Connections and feedback among urban residents and the responsive city are critical to Urban Informatics. One of the main modes of interaction between the public and Big Data streams is the ever-expanding suite of urban-focused smartphone applications. Governments are joining the app trend by hosting civic hackathons focused on app development. For all the attention and effort spent on app production and hackathons, however, a closer examination reveals a glaring irony of the Big Data age: to date, the results have been remarkably small in both scope and users. In this paper, we critically analyze the structure of The White House Hackathon, New York City BigApps, and the National Day of Civic Hacking, which are three recent, high-publicity hackathons in the United States. We propose a taxonomy of civic apps, analyze hackathon models and results against the taxonomy, and evaluate how the hackathon structure influences the apps produced. In particular, we examine problem definitions embedded in the different models and the issue of sustaining apps past the hackathon. We question the effectiveness of apps as the interface between urban data and urban residents, asking who is represented by and participates in the solutions offered by apps. We determine that the transparency, collaboration and innovation that hackathons aspire to are not yet fully realized, leading to the question: can civic Big Data lead to big impacts?

From 2019, countries worldwide have been negatively affected by the corona virus disease 2019 (COVID-19) in all aspects of social life. The high-tech digital industry represented by emerging digital technologies is still vigorous, and correspondingly, the digital economy has become an important force to promote the stable recovery and re-prosperity of the national economy. The digital economy plays a memorable role in preventing and controlling COVID-19, the resumption of work and production, and the creation of new business formats and models. Urban big data (UBD) involves a wide range of dynamic and static data with high dimensions, but there are no mature and clear data classification and grading standards. Currently, it is urgent to strengthen the security protection of high-value datasets. Therefore, a UBD classification and grading method is proposed based on the lightweight (LWT) deep learning (DL) clustering algorithm. It uses a semi-intelligent path based on partial artificial to form data classification (DC) and hierarchical thesaurus, corpus, rule base, and model base. Subsequently, a big data analysis system is built for unstructured and structured data association analysis based on deep learning, spatiotemporal correlation, and big data technology to improve data value and adapt to multiscenario applications. Meanwhile, with the help of data and graphics processing tool Tableau, the present work analyzes the development status and existing problems of digital resources in China. The results show that although China's digital infrastructure is the top in the world, the trading infrastructure is still only 41.65 percentage points. This shows that China's digital economy still has a lot of room for growth in distribution and trading. The analysis of the ownership of data resources indicates that the scores of China's digital economy in accounting, privacy, and security are very low, only 2.4 points, 5.1 points, and 11 points, respectively. This study has solved the problems of distribution and trade in China's digital economy through research and put forward corresponding suggestions for the current development of China's digital economy market. Hence, a preliminary summary and suggestions are made on the development of China's data resources, to promote the open sharing of data, strengthen the management of data quality, activate the data resource market, strengthen data security, and enhance the vitality of the market economy.

Identifying urban vitality is the key to optimizing the urban structure. Previous studies on urban multisource data and urban vitality often assume that they follow a predefined (linear or nonlinear in terms of parameters) relationship, and few studies have explored the causality of urban multisource data on urban vitality. The existing machine learning methods often pay attention to the correlation in the data and ignore the causality. With the continuous emergence of new needs, its disadvantages gradually begin to appear and face a series of urgent problems in interpretability, robustness, and fairness. In this paper, we use a combination of causal inference and machine learning to deeply explore and analyze the causal effects of multisource data on the 16 administrative districts of Shanghai, taking Shanghai as an example. The analysis results show that each data indicator has different degrees of influence on the urban vitality of the 16 administrative districts of Shanghai, resulting in different heterogeneous effects, and through the analysis result, each administrative district can better optimize urban resources and improve urban vitality according to its situation. This discovery guides urban planning and has enlightenment significance for cities seeking construction facility investment and facility construction‐oriented development.

Cities are responsible for approximately three-quarters of global greenhouse gas emissions and energy use. Urban metabolism is a research field that studies resource use and pollution emissions of cities, which is reliant on an important process of data collection and analysis. Accessing reliable and recent data is one of the main challenges for urban metabolism studies as it can affect their accuracy and comparability to other cities. The emergence of Open Data at an urban level is an exceptional opportunity for the field. Nevertheless, Open Data also present a number of challenges before they are fully and more easily integrated in current studies. This chapter discusses the opportunities and challenges of using Open Data in urban metabolism studies and consequently to urban environmental policymaking.

In this study, an experimental study (ESIF) is presented for urban data management and smart city application using energy-saved IoT big data analytics. This study is also used to manage the energy consumption in urban planning and smart city applications. The previous researchers demonstrate that the big data analysis process is more complex. We use different kinds of sensors such as traffic congestion, weather, smart home, water, and vehicular sensor to collect the resource data. The framework is divided into three phases. The first phase is responsible for collecting the data using IoT devices and interconnects with elasticity (energy-saving) manager. The second phase is data handling operation using MapReduce framework. Third phase deals with decision-making which is an important step to classify the events and produce the decisions accurately. To do this, machine learning concept of deep learning algorithms is used and it produces the decisions more effectively. The Hadoop is to provide offline service as well as online services with the help of spark. Therefore, the effectiveness of the model is illustrated in the case of processing time and throughput.