Spatial information and the legibility of urban form: Big data in urban morphology

Residential land structure affects residential welfare: Linear and non-linear effects

The non-equilibrium phenomenon of residential land structure should be accorded particular importance when discussing residential welfare. Based on balanced panel data at the provincial level in China from 2009 to 2017, this study constructed an indicator to measure the residential welfare level using a multi-dimensional approach. It explored residential land structure's impact on residential welfare and its mechanism of action under carbon emissions and urbanization from both linear and non-linear perspectives. An orderly residential land structure was found to significantly positively affect residential welfare and this effect varies among provincial cities. Per the mechanism analysis, in the process of the residential land structure's impact on residential welfare, urbanization's mediating effect is influenced by the environment, whereas carbon emissions' moderating effect is partially influenced by urbanization. These insights contribute to the residential welfare literature and provide actionable recommendations for policy implementation in developing regions.

How urban growth dynamics impact the air quality? A case of eight Indian metropolitan cities

Air pollution is a matter of great significance that confronts the sustainable progress of urban areas. Against India's swift urbanization, several urban areas exhibit the coexistence of escalating populace and expansion in developed regions alongside extensive spatial heterogeneity. The interaction mechanism between the growth of urban areas and the expansion of cities holds immense importance for the remediation of air pollution. Henceforth, the present investigation utilizes geographically weighted regression (GWR) to examine the influence of urban expansion and population growth on air quality. The examination will use a decade of data on the variation in PM2.5 levels from 2010 to 2020 in eight Indian metropolitan cities. The study's findings demonstrate a spatial heterogeneity between urban growth dynamics and air pollution levels. Urban growth and the expansion of cities demonstrate notable positive impacts on air quality, although the growth of infilling within expanding urban areas can significantly affect air quality. Given the unique trajectories of urban development in developing countries, this research provides many suggestions for urban administrators to foster sustainable urban growth.

The spatial and social correlates of neighborhood morphology: Evidence from building footprints in five U.S. metropolitan areas

Recent advances in quantitative tools for examining urban morphology enable the development of morphometrics that can characterize the size, shape, and placement of buildings; the relationships between them; and their association with broader patterns of development. Although these methods have the potential to provide substantial insight into the ways in which neighborhood morphology shapes the socioeconomic and demographic characteristics of neighborhoods and communities, this question is largely unexplored. Using building footprints in five of the ten largest U.S. metropolitan areas (Atlanta, Boston, Chicago, Houston, and Los Angeles) and the open-source R package, foot, we examine how neighborhood morphology differs across U.S. metropolitan areas and across the urban-exurban landscape. Principal components analysis, unsupervised classification (K-means), and Ordinary Least Squares regression analysis are used to develop a morphological typology of neighborhoods and to examine its association with the spatial, socioeconomic, and demographic characteristics of census tracts. Our findings illustrate substantial variation in the morphology of neighborhoods, both across the five metropolitan areas as well as between central cities, suburbs, and the urban fringe within each metropolitan area. We identify five different types of neighborhoods indicative of different stages of development and distributed unevenly across the urban landscape: these include low-density neighborhoods on the urban fringe; mixed use and high-density residential areas in central cities; and uniform residential neighborhoods in suburban cities. Results from regression analysis illustrate that the prevalence of each of these forms is closely associated with variation in socioeconomic and demographic characteristics such as population density, the prevalence of multifamily housing, and income, race/ethnicity, homeownership, and commuting by car. We conclude by discussing the implications of our findings and suggesting avenues for future research on neighborhood morphology, including ways that it might provide insight into issues such as zoning and land use, housing policy, and residential segregation.

A spatio-temporal analysis investigating completeness and inequalities of global urban building data in OpenStreetMap

OpenStreetMap (OSM) has evolved as a popular dataset for global urban analyses, such as assessing progress towards the Sustainable Development Goals. However, many analyses do not account for the uneven spatial coverage of existing data. We employ a machine-learning model to infer the completeness of OSM building stock data for 13,189 urban agglomerations worldwide. For 1,848 urban centres (16% of the urban population), OSM building footprint data exceeds 80% completeness, but completeness remains lower than 20% for 9,163 cities (48% of the urban population). Although OSM data inequalities have recently receded, partially as a result of humanitarian mapping efforts, a complex unequal pattern of spatial biases remains, which vary across various human development index groups, population sizes and geographic regions. Based on these results, we provide recommendations for data producers and urban analysts to manage the uneven coverage of OSM data, as well as a framework to support the assessment of completeness biases.

Smart City Applications to Promote Citizen Participation in City Management and Governance: A Systematic Review

This systematic review aimed to provide a comprehensive view of (1) the purposes of research studies using smart city infrastructures to promote citizen participation in the cities’ management and governance, (2) the characteristics of the proposed solutions in terms of data sources, data quality, and data security and privacy mechanisms, as well, as strategies to incentivize citizen participation, and (3) the development stages of the applications being reported. An electronic search was conducted combining relevant databases and keywords, and 76 studies were included after a selection process. The results show a current interest in developing applications to promote citizen participation to identify urban problems and contribute to decision-making processes. Most of the included studies considered citizens as agents able to report issues (e.g., issues related to the maintenance of urban infrastructures or the mobility in urban spaces), monitor certain environmental parameters (e.g., air or acoustic pollution), and share opinions (e.g., opinions about the performance of local authorities) to support city management. Moreover, a minority of the included studies developed collaborative applications to involve citizens in decision-making processes in urban planning, the selection of development projects, and deepening democratic values. It is possible to conclude about the existence of significant research related to the topic of this systematic review, but also about the need to deepen mechanisms to guarantee data quality and data security and privacy, to develop strategies to incentivize citizen participation, and to implement robust experimental set-ups to evaluate the impact of the developed applications in daily contexts.

Understanding the User-Generated Geographic Information by Utilizing Big Data Analytics for Health Care

There are two main ways to achieve an active lifestyle, the first is to make an effort to exercise and second is to have the activity as part of your daily routine. The study's major purpose is to examine the influence of various kinds of physical engagements on density dispersion of participants in Shanghai, China, and even prototype check-in data from a Location-Based Social Network (LBSN) utilizing a mix of spatial, temporal, and visualization methodologies. This paper evaluates Weibo used for big data evaluation and its dependability in some types rather than physically collected proofs by investigating the relationship between time, class, place, frequency, and place of check-in built on geographic features and related consequences. Kernel density estimation has been used for geographical assessment. Physical activities and frequency allocation are formed as a result of hour-to-day consumption habits. Our observations are based on customer check-in activities in physical venues such as gyms, parks, and playing fields, the prevalence of check-ins, peak times for visiting fun parks, and gender disparities, and we applied relative difference formulation to reveal the gender difference in a much better way. The purpose of this research is to investigate the influence of physical activity and health-related standard of living on well-being in a selection of Shanghai inhabitants.

Extraction of Urban Built-Up Area Based on Deep Learning and Multi-Sources Data Fusion—The Application of an Emerging Technology in Urban Planning

With the rapid expansion of urban built-up areas in recent years, it has become particularly urgent to develop a fast, accurate and popularized urban built-up area extraction method system. As the direct carrier of urban regional relationship, urban built-up area is an important reference to judge the level of urban development. The accurate extraction of urban built-up area plays an important role in formulating scientific planning thus to promote the healthy development of both urban area and rural area. Although nighttime light (NTL) data are used to extract urban built-up areas in previous studies, there are certain shortcomings in using NTL data to extract urban built-up areas. On the other hand, point of interest (POI) data and population migration data represent different attributes in urban space, which can both assist in modifying the deficiencies of NTL data from both static and dynamic spatial elements, respectively, so as to improve the extraction accuracy of urban built-up areas. Therefore, this study attempts to propose a feasible method to modify NTL data by fusing Baidu migration (BM) data and POI data thus accurately extracting urban built-up areas in Guangzhou. More accurate urban built-up areas are extracted using the method of U-net deep learning network. The maximum built-up area extracted from the study is 1103.45 km2, accounting for 95.21% of the total built-up area, and the recall rate is 0.8905, the precision rate is 0.8121, and the F1 score is 0.8321. The results of using POI data and BM data to modify NTL data to extract built-up areas have not been significantly improved due to the fact that the more data get fused, the more noise there would be, which would ultimately affect the results. This study analyzes the feasibility and insufficiency of using big data to modify NTL data through data fusion and feature extraction system, which has important theoretical and practical significance for future studies on urban built-up areas and urban development.

The Legibility Efficacy of Historical Neighborhoods in Creating a Cognitive Map for Citizens

Nowadays, historical neighborhoods in Iran are in a state of decay due to the lack of spatial legibility with respect to their organic spatial layout. Hence, the level of interaction among people and their perception of the environment has gradually diminished. Historical neighborhoods no longer possess their former prosperity and function, and citizens prefer to inhabit neighborhoods with newer configurations to meet their demands. The aim of this study was to investigate the role of the spatial legibility of historical neighborhoods in creating a suitable cognitive map for citizens. Quantitative and qualitative methods were applied using a questionnaire and simulation test instruments. In addition, in order to analyze the spatial configuration of the target areas, the Space Syntax method was implemented using Depthmap 10. The findings indicated that landmarks and navigation signs were identified as the most important factors affecting legibility from the viewpoint of users. Furthermore, the correlation between integration and connectivity illustrated that the studied areas possessed poor spatial legibility due to the organic texture of their spatial configurations. Accordingly, solutions such as enhancing the interaction between highly integrated streets and identified landmarks were emphasized to ameliorate the legibility of these spaces. The present study contributes to sustainable urban design with respect to the rejuvenation of historical districts by proposing a set of pragmatic alterations of urban spatial configurations. This research may be fruitful for urban designers, planners and managers seeking to revitalize historical neighborhoods.

Using open data and open-source software to develop spatial indicators of urban design and transport features for achieving healthy and sustainable cities

Benchmarking and monitoring of urban design and transport features is crucial to achieving local and international health and sustainability goals. However, most urban indicator frameworks use coarse spatial scales that either only allow between-city comparisons, or require expensive, technical, local spatial analyses for within-city comparisons. This study developed a reusable, open-source urban indicator computational framework using open data to enable consistent local and global comparative analyses. We show this framework by calculating spatial indicators-for 25 diverse cities in 19 countries-of urban design and transport features that support health and sustainability. We link these indicators to cities' policy contexts, and identify populations living above and below critical thresholds for physical activity through walking. Efforts to broaden participation in crowdsourcing data and to calculate globally consistent indicators are essential for planning evidence-informed urban interventions, monitoring policy effects, and learning lessons from peer cities to achieve health, equity, and sustainability goals.

Big Data-Driven Urban Management: Potential for Urban Sustainability

With the world’s rapidly growing urbanization, urban sustainability is now expected for urban life. Due to this rapid growth, meeting the emerging challenges for urban management and sustainability worldwide is challenging. Big data-driven technologies can be an excellent solution to address these upcoming challenges. Therefore, this study explores the potential of big data technologies for ensuring sustainability in urban management. The study conducted a systematic literature review guided by PRISMA (preferred reporting items for systematic review and meta-analysis) on publications over the last 21 years. The study argues that urban management is an integrated function of public and private agencies to address the significant challenges of urban life and to develop the city as more competitive, habitable, and sustainable. Urban management can utilize big data analytics (BDA) for digital instrumentation, data-informed policy decisions, governance, real-time management, and evidence-based decisions. Urban sustainability can ensure the smooth operation of urban affairs through strategic planning under three major dimensions: social, economic, and environmental. Big data technologies can ensure smart transport, traffic, waste management, energy, environment, infrastructure, safety, healthcare, planning, and citizen participation in regular urban affairs to provide a better urban life. This study develops several indicators that will be helpful for concerned stakeholders in policy, planning, designing, and implementing sustainable urban development.

The potential of implementing superblocks for multifunctional street use in cities

The Barcelona superblock has been proposed as a sustainable urban neighbourhood transformation strategy in cities. Superblock design reduces space assigned to cars to enable alternative uses for improving liveability and sustainability. Here, the potential for superblock transformation is systematically quantified and evaluated for cities with varying urban forms and densities. A superblock consists of nine (3×3) urban city blocks including interior and exterior streets. Miniblocks, consisting of four (2×2) blocks, are proposed as a less disruptive strategy to initiate urban transformation on which superblocks can build upon. A geospatial network-based approach is developed to find locations for introducing multifunctional streets. For possible site prioritization, the identified locations are evaluated concerning the potential disruption to traffic. The analysis reveals that the potential for super- and miniblocks, as well as their disruption effect, varies considerably across cities and is affected by the urban layout. For some cities, over 40% of the street network is potentially suitable for integrating super- or miniblock design, providing opportunities for city-scale transition towards more sustainable and liveable cities. A grid-like layout in cities is not a sufficient condition for high superblock potential and cities with irregular street layouts can show high transformation potential as well.

Revolutionary Strategies Analysis and Proposed System for Future Infrastructure in Internet of Things

The Internet of Things (IoT) has changed the worldwide network of people, smart devices, intelligent things, data, and information as an emergent technology. IoT development is still in its early stages, and numerous interrelated challenges must be addressed. IoT is the unifying idea of embedding everything. The Internet of Things offers a huge opportunity to improve the world’s accessibility, integrity, availability, scalability, confidentiality, and interoperability. However, securing the Internet of Things is a difficult issue. The IoT aims to connect almost everything within the framework of a common infrastructure. This helps in controlling devices and, will allow device status to be updated everywhere and at any time. To develop technology via IoT, several critical scientific studies and inquiries have been carried out. However, many obstacles and problems remain to be tackled in order to reach IoT’s maximum potential. These problems and concerns must be taken into consideration in different areas of the IoT, such as implementation in remote areas, threats to the system, development support, social and environmental impacts, etc. This paper reviews the current state of the art in different IoT architectures, with a focus on current technologies, applications, challenges, IoT protocols, and opportunities. As a result, a detailed taxonomy of IoT is presented here which includes interoperability, scalability, security and energy efficiency, among other things. Moreover, the significance of blockchains and big data as well as their analysis in relation to IoT, is discussed. This article aims to help readers and researchers understand the IoT and its applicability to the real world.

Experimentation Platforms as Bridges to Urban Sustainability

Despite immense efforts to realize diverse visions of the ‘smart city,’ municipalities still face manifold uncertainties of how governance and the tools of governance can best support public and regional value creation for achieving urban sustainability. To this end, Urban Living Labs have become a known enabling mechanism. In this paper, we extend the lab idea and formulate the concept of Urban Experimentation Platform that focuses on developing urban innovation ecosystems for urban sustainability. We use action design research and participant observation across multiple case studies enacting Urban Experimentation Platforms in order to investigate how the tie-in between governance and the local lab’s innovation process unfolds. Our analysis distills three facets that are instrumental in institutionalizing these platforms as resilient organizational models. With the help of the case studies, we illustrate the three facets, concerning issues of urban ecosystem governance, empowering co-creation, and qualifying local innovation. The facets reinforce the roles of digital instruments and digital capabilities for effective urban governance and platform management. We draw some conclusions for future research and formulate policy recommendations for implementing and operating Urban Experimentation Platforms.

Public health in times of crisis: An overlooked variable in city management theories?

The volume of research that associates the theme of city management with crises resulting from emerging infectious disease is modest, even after the occurrences of Ebola and Severe Acute Respiratory Syndrome. Similarly, the Coronavirus disease (COVID-19) pandemic has thus far contributed only modestly to the expansion of attention to people's health, through city management, in times of crisis. This study, by means of a systematic literature review, analyzes the gap in research on urban theory on how epidemics are confronted. The term "cities" had 2,440,607 articles published and were identified 665 that presents the combination of the term "pandemic". After the development of content analysis were identified 11 articles prior to 2019 and 10 articles published between January and June 2020, adhering to the objective of this investigation. Prior to 2019 studies addressed topics related to the construction of an urban structure aimed at reducing people's vulnerability to infectious diseases, starting in 2020, the focus of researchers' attention is on the use of information and communication technologies used as tools for prevention and control. Theories of the management of cities indicate the need to extrapolate the urban perimeter, incorporating the relations of dependence in cities with the other actors within the surroundings, especially in times of crisis. Studies have emphasized that cities are not isolated islands; rather, they are parts of a complex system with multiple exchanges. This thematic field of study enhances research that presents urban planning solutions by using data-driven management to consider conduct, parameters, and protocols relating to public health in moments of crisis.

Opportunities and Challenges of Geospatial Analysis for Promoting Urban Livability in the Era of Big Data and Machine Learning

Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challenges of urban complex systems requires advanced interdisciplinary analysis methods, such as urban informatics or urban data science. Yet, by applying a purely data-driven approach, it is too easy to get lost in the ‘forest’ of data, and to miss the ‘trees’ of successful, livable cities that are the ultimate aim of urban planning. This paper assesses how geospatial data, and urban analysis, using a mixed methods approach, can help to better understand urban dynamics and human behavior, and how it can assist planning efforts to improve livability. Based on reviewing state-of-the-art research the paper goes one step further and also addresses the potential as well as limitations of new data sources in urban analytics to get a better overview of the whole ‘forest’ of these new data sources and analysis methods. The main discussion revolves around the reliability of using big data from social media platforms or sensors, and how information can be extracted from massive amounts of data through novel analysis methods, such as machine learning, for better-informed decision making aiming at urban livability improvement.

The Role of Urban Morphology Design on Enhancing Physical Activity and Public Health

Along with environmental pollution, urban planning has been connected to public health. The research indicates that the quality of built environments plays an important role in reducing mental disorders and overall health. The structure and shape of the city are considered as one of the factors influencing happiness and health in urban communities and the type of the daily activities of citizens. The aim of this study was to promote physical activity in the main structure of the city via urban design in a way that the main form and morphology of the city can encourage citizens to move around and have physical activity within the city. Functional, physical, cultural-social, and perceptual-visual features are regarded as the most important and effective criteria in increasing physical activities in urban spaces, based on literature review. The environmental quality of urban spaces and their role in the physical activities of citizens in urban spaces were assessed by using the questionnaire tool and analytical network process (ANP) of structural equation modeling. Further, the space syntax method was utilized to evaluate the role of the spatial integration of urban spaces on improving physical activities. Based on the results, consideration of functional diversity, spatial flexibility and integration, security, and the aesthetic and visual quality of urban spaces plays an important role in improving the physical health of citizens in urban spaces. Further, more physical activities, including motivation for walking and the sense of public health and happiness, were observed in the streets having higher linkage and space syntax indexes with their surrounding texture.

Understanding Chinese Urban Form: The Universal Fractal Pattern of Street Networks over 298 Cities

Urban form can be reflected by many city elements, such as streets. A street network serves as the backbone of a city and reflects a city’s physical structure. A street network’s topological measures and statistical distributions have been widely investigated in recent years, but previous studies have seldom characterized the heavy-tailed distribution of street connectivities from a fractal perspective. The long-tail distribution of street connectivities can be fractal under the new, third definition: a set or pattern is fractal if the scaling of far more small things than large ones recurs at least twice. The number of recurred scaling patterns of far more less-connected streets than well-connected ones greatly helps in measuring the scaling hierarchy of a street network. Moreover, it enables us to examine the potential fractality of urban street networks at the national scale. In this connection, the present study aims to contribute to urban morphology in China through the investigation of the ubiquity of fractal cities from the lens of street networks. To do this, we generate hundreds of thousands of natural streets from about 4.5 million street segments over 298 Chinese cities and adopted power-law detection as well as three fractal metrics that emerged from the third definition of fractal. The results show that almost all cities bear a fractal structure in terms of street connectivities. Furthermore, our multiple regression analysis suggests that the fractality of street networks is positively correlated with urban socioeconomic status and negatively correlated with energy consumption. Therefore, the fractal metrics can be a useful supplement to traditional street-network configuration measures such as street lengths.