A Study on Upgrading Non-urban Areas-Using Big Data the Case of Hwang Ze and Danggok Districts

2018 ◽  
pp. 189-205
Author(s):  
Yong Pil Geum
Keyword(s):  
Big Data ◽  
Urban Areas

scholarly journals Understanding policing demand and deployment through the lens of the city and with the application of big data

Urban Studies ◽  
2021 ◽  
pp. 004209802098100
Author(s):  
Mark Ellison ◽  
Jon Bannister ◽  
Won Do Lee ◽  
Muhammad Salman Haleem
Keyword(s):  
Big Data ◽  
Urban Environment ◽  
Urban Areas ◽  
Multilevel Model ◽  
Open Data ◽  
Front Line ◽  
Criminological Research ◽  
The Social ◽  
Generalised Method Of Moments ◽  
The City

The effective, efficient and equitable policing of urban areas rests on an appreciation of the qualities and scale of, as well as the factors shaping, demand. It also requires an appreciation of the factors shaping the resources deployed in their address. To this end, this article probes the extent to which policing demand (crime, anti-social behaviour, public safety and welfare) and deployment (front-line resource) are similarly conditioned by the social and physical urban environment, and by incident complexity. The prospect of exploring policing demand, deployment and their interplay is opened through the utilisation of big data and artificial intelligence and their integration with administrative and open data sources in a generalised method of moments (GMM) multilevel model. The research finds that policing demand and deployment hold varying and time-sensitive association with features of the urban environment. Moreover, we find that the complexities embedded in policing demands serve to shape both the cumulative and marginal resources expended in their address. Beyond their substantive policy relevance, these findings serve to open new avenues for urban criminological research centred on the consideration of the interplay between policing demand and deployment.

scholarly journals Optimization of Evacuation and Walking-Home Routes from Osaka City After a Nankai Megathrust Earthquake Using Road Network Big Data

2021 ◽  
pp. 369-389
Author(s):  
Atsushi Takizawa ◽  
Yutaka Kawagishi
Keyword(s):  
Big Data ◽  
Public Transportation ◽  
Urban Areas ◽  
Road Network ◽  
Large Earthquake ◽  
The Road ◽  
Return Home ◽  
Flooded Area ◽  
Network Big Data ◽  
Surrounding Areas

AbstractWhen a disaster such as a large earthquake occurs, the resulting breakdown in public transportation leaves urban areas with many people who are struggling to return home. With people from various surrounding areas gathered in the city, unusually heavy congestion may occur on the roads when the commuters start to return home all at once on foot. In this chapter, it is assumed that a large earthquake caused by the Nankai Trough occurs at 2 p.m. on a weekday in Osaka City, where there are many commuters. We then assume a scenario in which evacuation from a resulting tsunami is carried out in the flooded area and people return home on foot in the other areas. At this time, evacuation and returning-home routes with the shortest possible travel times are obtained by solving the evacuation planning problem. However, the road network big data for Osaka City make such optimization difficult. Therefore, we propose methods for simplifying the large network while keeping those properties necessary for solving the optimization problem and then recovering the network. The obtained routes are then verified by large-scale pedestrian simulation, and the effect of the optimization is verified.

The Use of Social Media for Urban Planning

2019 ◽  
pp. 1049-1070
Author(s):  
Fabian Neuhaus
Keyword(s):  
Social Media ◽  
Big Data ◽  
Urban Areas ◽  
Large Scale ◽  
Urban Environments ◽  
Data Systems ◽  
User Management ◽  
Social Nature ◽  
User Data ◽  
Use Of Social Media

User data created in the digital context has increasingly been of interest to analysis and spatial analysis in particular. Large scale computer user management systems such as digital ticketing and social networking are creating vast amount of data. Such data systems can contain information generated by potentially millions of individuals. This kind of data has been termed big data. The analysis of big data can in its spatial but also in a temporal and social nature be of much interest for analysis in the context of cities and urban areas. This chapter discusses this potential along with a selection of sample work and an in-depth case study. Hereby the focus is mainly on the use and employment of insight gained from social media data, especially the Twitter platform, in regards to cities and urban environments. The first part of the chapter discusses a range of examples that make use of big data and the mapping of digital social network data. The second part discusses the way the data is collected and processed. An important section is dedicated to the aspects of ethical considerations. A summary and an outlook are discussed at the end.

Applications of Big Data and Green IoT-Enabling Technologies for Smart Cities

2019 ◽  
pp. 22-41
Author(s):  
Onur Dogan ◽  
Omer Faruk Gurcan
Keyword(s):  
Big Data ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Smart Cities ◽  
Digital Data ◽  
Rapid Urbanization ◽  
Data Resource ◽  
Parallel Data ◽  
Information And Communication

In recent years, enormous amounts of digital data have been generated. In parallel, data collection, storage, and analysis technologies have developed. Recently, there has been an increasing trend of people moving towards urban areas. By 2030 more than 60% of the world's population will live in an urban environment. Urban areas are big data resource because they include millions of citizens, technological devices, and vehicles which generate data continuously. Besides, rapid urbanization brings many challenges, such as environmental pollution, traffic congestion, health problems, energy management, etc. Some policies for countries are required to cope with urbanization problems. One of these policies is to build smart cities. Smart cities integrate information and communication technology and various physical devices connected to the network (the internet of things or IoT) to both improve the quality of government services and citizen welfare. This chapter presents a literature review of big data, smart cities, IoT, green-IoT concepts, using technology and methods, and applications worldwide.

scholarly journals Measuring Functional Urban Shrinkage with Multi-Source Geospatial Big Data: A Case Study of the Beijing-Tianjin-Hebei Megaregion

2020 ◽  
Vol 12 (16) ◽  
pp. 2513 ◽  
Author(s):  
Qiwei Ma ◽  
Zhaoya Gong ◽  
Jing Kang ◽  
Ran Tao ◽  
Anrong Dang
Keyword(s):  
Big Data ◽  
Urban Areas ◽  
Spatial Clustering ◽  
Landscape Fragmentation ◽  
Analytical Framework ◽  
Urban Land Use ◽  
Urban Systems ◽  
Landsat 8 ◽  
Clustering Methods

Most of the shrinking cities experience an unbalanced deurbanization across different urban areas in cities. However, traditional ways of measuring urban shrinkage are focused on tracking population loss at the city level and are unable to capture the spatially heterogeneous shrinking patterns inside a city. Consequently, the spatial mechanism and patterns of urban shrinkage inside a city remain less understood, which is unhelpful for developing accommodation strategies for shrinkage. The smart city initiatives and practices have provided a rich pool of geospatial big data resources and technologies to tackle the complexity of urban systems. Given this context, we propose a new measure for the delineation of shrinking areas within cities by introducing a new concept of functional urban shrinkage, which aims to capture the mismatch between urban built-up areas and the areas where significantly intensive human activities take place. Taking advantage of a data fusion approach to integrating multi-source geospatial big data and survey data, a general analytical framework is developed to construct functional shrinkage measures. Specifically, Landsat-8 remote sensing images were used for extracting urban built-up areas by supervised neural network classifications and Geographic Information System tools, while cellular signaling data from China Unicom Inc. was used to depict human activity areas generated by spatial clustering methods. Combining geospatial big data with urban land-use functions obtained from land surveys and Points-Of-Interests data, the framework further enables the comparison between cities from dimensions characterized by indices of spatial and urban functional characteristics and the landscape fragmentation; thus, it has the capacity to facilitate an in-depth investigation of fundamental causes and internal mechanisms of urban shrinkage. With a case study of the Beijing-Tianjin-Hebei megaregion using data from various sources collected for the year of 2018, we demonstrate the validity of this approach and its potential generalizability for other spatial contexts in facilitating timely and better-informed planning decision support.

The big data regime shift in real estate

2020 ◽  
Vol 38 (4) ◽  
pp. 363-395 ◽  
Author(s):  
James R. DeLisle ◽  
Brent Never ◽  
Terry V. Grissom
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Real Estate ◽  
Urban Areas ◽  
Regime Shift ◽  
Design Thinking ◽  
Future Research ◽  
Content Type ◽  
Data Movement ◽  
Support Decision Making

PurposeThe paper explores the emergence of the “big data regime” and the disruption that it is causing for the real estate industry. The paper defines big data and illustrates how an inductive, big data approach can help improve decision-making.Design/methodology/approachThe paper demonstrates how big data can support inductive reasoning that can lead to enhanced real estate decisions. To help readers understand the dynamics and drivers of the big data regime shift, an extensive list of hyperlinks is included.FindingsThe paper concludes that it is possible to blend traditional and non-traditional data into a unified data environment to support enhanced decision-making. Through the application of design thinking, the paper illustrates how socially responsible development can be targeted to under-served urban areas and helps serve residents and the communities in which they live.Research limitations/implicationsThe paper demonstrates how big data can be harnessed to support decision-making using a hypothetical project. The paper does not present advanced analytics but focuses aggregating disparate longitudinal data that could support such analysis in future research.Practical implicationsThe paper focuses on the US market, but the methodology can be extended to other markets where big data is increasingly available.Social implicationsThe paper illustrates how big data analytics can be used to help serve the needs of marginalized residents and tenants, as well as blighted areas.Originality/valueThis paper documents the big data movement and demonstrates how non-traditional data can support decision-making.

Modelling and simulation techniques for transport networks within urban areas applied to logistics and resilience

2021 ◽  
Author(s):  
◽  
Pablo Álvarez
Keyword(s):  
Big Data ◽  
Real Time ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Smart Cities ◽  
Modelling And Simulation ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Simulation Techniques ◽  
New Methodologies

This thesis investigates the use of modelling and simulation techniques in urban areas of smart cities, also exploring how big data can be used to feed these models. These modelling techniques have been applied to two different fields that have been gaining prominence during the last years but where research is still limited: urban logistics and urban resilience. Through this thesis, the author has expanded the research knowledge in these fields by exploring different methods such as meta-heuristics, transport modelling, and agent-based simulation in order to define new methodologies to be applied to urban areas. Regarding logistics, the author has shown through the use of meta-heuristics that when traffic congestion is considered as a dynamic attribute to optimize delivery routes in urban areas, time can be reduced by 11%, which is crucial for logistics companies in a market that is fiercer every day. This is true not only for urban areas, but this research has also demonstrated that optimizing routes with dynamic congestion attributes is also beneficial at a strategic level for routes between cities. To consider congestion costs in real time, a new approach has been developed in which data from Google is downloaded to feed these meta-heuristic models, although other sources of big data could be also used. In this thesis, a methodology is also presented that has been used to model logistics routes in urban areas considering real-time data and with the flexibility to add different network attributes (gradient, traffic bans, CO2, etc.) to simulate different scenarios. This can be useful for logistics companies to optimize their deliveries (choosing between van or tricycles, selecting the time of the day to deliver, etc.) but also for public authorities to get guidance on different transport and urban policies (pedestrianization of some streets, traffic bans, etc.).As for city resilience, the thesis focuses on evacuation planning. A new methodology has been created in which agent-based simulation is used through interconnected sub-models to model a large-scenario evacuation scenario (flooding event as a consequence of a dam collapse). This research defines the data needed to create these models that can be of great help to improve city resilience, and also analyzes how traffic congestion can affect the evacuation procedures. Through the different research articles that compose this thesis, the author brings light to these fields by developing new methodologies and using real case-studies that can help urban planners, companies, and policy makers to create more efficient, sustainable, and resilient smart cities.

The Use of Social Media for Urban Planning

2014 ◽  
pp. 113-134 ◽  
Author(s):  
Fabian Neuhaus
Keyword(s):  
Social Media ◽  
Big Data ◽  
Urban Areas ◽  
Large Scale ◽  
Urban Environments ◽  
Data Systems ◽  
User Management ◽  
Social Nature ◽  
User Data ◽  
Use Of Social Media

User data created in the digital context has increasingly been of interest to analysis and spatial analysis in particular. Large scale computer user management systems such as digital ticketing and social networking are creating vast amount of data. Such data systems can contain information generated by potentially millions of individuals. This kind of data has been termed big data. The analysis of big data can in its spatial but also in a temporal and social nature be of much interest for analysis in the context of cities and urban areas. This chapter discusses this potential along with a selection of sample work and an in-depth case study. Hereby the focus is mainly on the use and employment of insight gained from social media data, especially the Twitter platform, in regards to cities and urban environments. The first part of the chapter discusses a range of examples that make use of big data and the mapping of digital social network data. The second part discusses the way the data is collected and processed. An important section is dedicated to the aspects of ethical considerations. A summary and an outlook are discussed at the end.

scholarly journals A novel big data approach to measure and visualize urban accessibility

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Yuqin Jiang ◽  
Diansheng Guo ◽  
Zhenlong Li ◽  
Michael E. Hodgson
Keyword(s):  
New York ◽  
Big Data ◽  
Urban Areas ◽  
Public Transit ◽  
Spatiotemporal Resolution ◽  
Diurnal Variability ◽  
Relative Index ◽  
Transit Accessibility ◽  
Urban Accessibility ◽  
Bus Services

AbstractAccessibility is a topic of interest to multiple disciplines for a long time. In the last decade, the increasing availability of data may have exceeded the development of accessibility modeling approaches, resulting in a modeling gap. In part, this modeling gap may have resulted from the differences needed for single versus multimodal opportunities for access to services. With a focus on large volumes of transportation data, a new measurement approach, called Urban Accessibility Relative Index (UARI), was developed for the integration of multi-mode transportation big data, including taxi, bus, and subway, to quantify, visualize and understand the spatiotemporal patterns of accessibility in urban areas. Using New York City (NYC) as the case study, this paper applies the UARI to the NYC data at a 500-m spatial resolution and an hourly temporal resolution. These high spatiotemporal resolution UARI maps enable us to measure, visualize, and compare the variability of transportation service accessibility in NYC across space and time. Results demonstrate that subways have a higher impact on public transit accessibility than bus services. Also, the UARI is greatly affected by diurnal variability of public transit service.

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