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.

scholarly journals An Evaluating Method of Public Transit Accessibility for Urban Areas Based on GIS

2016 ◽  
Vol 137 ◽  
pp. 132-140 ◽  
Author(s):  
Chen Yan-yan ◽  
Wei Pan-yi ◽  
Lai Jian-hui ◽  
Feng Guo-chen ◽  
Li Xin ◽  
...  
Keyword(s):  
Urban Areas ◽  
Public Transit ◽  
Transit Accessibility ◽  
Evaluating Method

scholarly journals Cartographying the real metropolis: A proposal for a data-based planning beyond the administrative boundaries

2017 ◽  
Author(s):  
Juan Ramón Selva-Royo ◽  
Nuño Mardones ◽  
Alberto Cendoya
Keyword(s):  
New York ◽  
Big Data ◽  
International Development ◽  
Urban Areas ◽  
Good Governance ◽  
Urban Expansion ◽  
Remotely Sensed Data ◽  
Current Case ◽  
The Real ◽  
Urban Extent

Cartographying the real metropolis: A proposal for a data-based planning beyond the administrative boundaries. Juan R. Selva-Royo¹, Nuño Mardones¹, Alberto Cendoya² ¹University of Navarra, School of Architecture, Department of Theory and Design, University of Navarra Campus, 31080 Pamplona, Spain; ²University of Navarra, ICS, Navarra Center for International Development, University of Navarra Campus, 31080, Pamplona, Spain E-mail: [email protected], [email protected], [email protected] Keywords (3-5): Data planning, metropolitan areas, big data, urban extent, good governance Conference topics and scale: Cartography and big data   Nowadays, there is a great gap between the functional reality of urban agglomerations and their planning, largely because of the traditional linkage of urban management to the administrative limits inherited from the past. It is also true that the regulation of urban activities, including census and statistical information, requires a closer view of its citizens that can only be addressed from the municipal level. In any case, it is clear that the metropolitan delimitation has met useful but often ethereal or exclusionary criteria (economic or labor patterns, functional areas...), which become disfigured by an administrative reality that does not always correspond to the real metropolis. This paper, aware of the new cartographic possibilities linked to the big data - CORINE Land Cover, SIOSE, multi-sector digital atlases (in many cases referred to the urban extent, etc.) and other open system platforms - explores the evidence that might base a new objective methodology for the delimitation and planning of large urban areas. Indeed, what if basic data for cities would arise not from administrative entities but from independent outside approaches such as satellite imagery? What if every single sensing unit (every citizen, company, building or vehicle) directly issued relevant and dynamic information without going through the municipal collection? Finally, the research analyzes the eventual implications of this data-based planning with administrative structures and urban planning competencies in force through some current case studies, with the purpose of achieving a more efficient and clear metropolitan governance for our planet.  References (100 words) Aguado, M. (coord.) (2012) Áreas Urbanas +50. Información estadística de las Grandes Áreas Urbanas españolas 2012 (Centro de Publicaciones Secretaría General Técnica Ministerio de Fomento, Madrid). Angel, S. (dir.) (2016) Atlas of Urban Expansion (http://www.atlasofurbanexpansion.org) accessed 29 January 2017. Brenner, N. and Katsikis, N. (2017) Is the World Urban? Towards a Critique of Geospatial Ideology (Actar Publishers, New York). Florczyk, A. J., Ferri, S., Syrris, V., Kemper, T., Halkia, M., Soille, P., and Pesaresi, M. (2016). ‘A New European Settlement Map from Optical Remotely Sensed Data’, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, 1978-1992. 

scholarly journals Development of the Real-time On-road Emission (ROE v1.0) model for street-scale air quality modeling based on dynamic traffic big data

2020 ◽  
Vol 13 (1) ◽  
pp. 23-40
Author(s):  
Luolin Wu ◽  
Ming Chang ◽  
Xuemei Wang ◽  
Jian Hang ◽  
Jinpu Zhang ◽  
...  
Keyword(s):  
Big Data ◽  
Air Quality ◽  
Real Time ◽  
Urban Areas ◽  
Vehicle Emissions ◽  
Heavy Duty ◽  
Spatiotemporal Resolution ◽  
The Real ◽  
The Impact ◽  
Road Emission

Abstract. Rapid urbanization in China has led to heavy traffic flows in street networks within cities, especially in eastern China, the economically developed region. This has increased the risk of exposure to vehicle-related pollutants. To evaluate the impact of vehicle emissions and provide an on-road emission inventory with higher spatiotemporal resolution for street-network air quality models, in this study, we developed the Real-time On-road Emission (ROE v1.0) model to calculate street-scale on-road hot emissions by using real-time big data for traffic provided by the Gaode Map navigation application. This Python-based model obtains street-scale traffic data from the map application programming interface (API), which are open-access and updated every minute for each road segment. The results of application of the model to Guangzhou, one of the three major cities in China, showed on-road vehicle emissions of carbon monoxide (CO), nitrogen oxide (NOx), hydrocarbons (HCs), PM2.5, and PM10 to be 35.22×104, 12.05×104, 4.10×104, 0.49×104, and 0.55×104 Mg yr−1, respectively. The spatial distribution reveals that the emission hotspots are located in some highway-intensive areas and suburban town centers. Emission contribution shows that the dominant contributors are light-duty vehicles (LDVs) and heavy-duty vehicles (HDVs) in urban areas and LDVs and heavy-duty trucks (HDTs) in suburban areas, indicating that the traffic control policies regarding trucks in urban areas are effective. In this study, the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) was applied to investigate the impact of traffic volume change on street-scale photochemistry in the urban areas by using the on-road emission results from the ROE model. The modeling results indicate that the daytime NOx concentrations on national holidays are 26.5 % and 9.1 % lower than those on normal weekdays and normal weekends, respectively. Conversely, the national holiday O3 concentrations exceed normal weekday and normal weekend amounts by 13.9 % and 10.6 %, respectively, owing to changes in the ratio of emission of volatile organic compounds (VOCs) and NOx. Thus, not only the on-road emissions but also other emissions should be controlled in order to improve the air quality in Guangzhou. More significantly, the newly developed ROE model may provide promising and effective methodologies for analyzing real-time street-level traffic emissions and high-resolution air quality assessment for more typical cities or urban districts.

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 Study of Temporal Variations of Equivalent Black Carbon in a Coastal City in Northwest Spain Using an Atmospheric Aerosol Data Management Software

2021 ◽  
Vol 11 (2) ◽  
pp. 516
Author(s):  
María Piñeiro-Iglesias ◽  
Javier Andrade-Garda ◽  
Sonia Suárez-Garaboa ◽  
Soledad Muniategui-Lorenzo ◽  
Purificación López-Mahía ◽  
...  
Keyword(s):  
Data Management ◽  
Black Carbon ◽  
Light Absorption ◽  
Atmospheric Aerosol ◽  
Urban Areas ◽  
Radiative Properties ◽  
Anthropogenic Sources ◽  
Biomass Combustion ◽  
Management Support ◽  
Diurnal Variability

Light-absorbing carbonaceous aerosols (including black carbon (BC)) pose serious health issues and play significant roles in atmospheric radiative properties. Two-year measurements (2015–2016) of aerosol light absorption, combined with measurements of sub-micrometric particles, were continuously conducted in A Coruña (northwest (NW) Spain) to determine their light absorption properties: absorption coefficients (σabs) and the absorption Ångström exponent (AAE). The mean and standard deviation of equivalent black carbon (eBC) during the period of study were 0.85 ± 0.83 µg m−3, which are lower than other values measured in urban areas of Spain and Europe. High eBC concentrations found in winter are associated with an increase in emissions from anthropogenic sources in combination with lower mixing layer heights and frequent stagnant conditions. The pronounced diurnal variability suggests a strong influence from local sources. AAE had an average value of 1.26 ± 0.22 which implies that both fossil fuel combustion and biomass burning influenced optical aerosol properties. This also highlights biomass combustion in suburban areas, where the use of wood for domestic heating is encouraged, as an important source of eBC. All data treatment was gathered using SCALA© as atmospheric aerosol data management support software program.

scholarly journals Measuring Accessibility Based on Improved Impedance and Attractive Functions Using Taxi Trajectory Data

2020 ◽  
Vol 13 (1) ◽  
pp. 112
Author(s):  
Helai Huang ◽  
Jialing Wu ◽  
Fang Liu ◽  
Yiwei Wang
Keyword(s):  
Waiting Time ◽  
Urban Areas ◽  
Transportation Systems ◽  
Trajectory Data ◽  
Peak Period ◽  
Important Indicator ◽  
Destination Attractiveness ◽  
Urban Accessibility ◽  
Spatio Temporal ◽  
Taxi Trajectory

Accessibility has attracted wide interest from urban planners and transportation engineers. It is an important indicator to support the development of sustainable policies for transportation systems in major events, such as the COVID-19 pandemic. Taxis are a vital travel mode in urban areas that provide door-to-door services for individuals to perform urban activities. This study, with taxi trajectory data, proposes an improved method to evaluate dynamic accessibility depending on traditional location-based measures. A new impedance function is introduced by taking characteristics of the taxi system into account, such as passenger waiting time and the taxi fare rule. An improved attraction function is formulated by considering dynamic availability intensity. Besides, we generate five accessibility scenarios containing different indicators to compare the variation of accessibility. A case study is conducted with the data from Shenzhen, China. The results show that the proposed method found reduced urban accessibility, but with a higher value in southern center areas during the evening peak period due to short passenger waiting time and high destination attractiveness. Each spatio-temporal indicator has an influence on the variation in accessibility.

scholarly journals Public Transit Loyalty Modeling Considering the Effect of Passengers’ Emotional Value: A Case Study in Xiamen, China

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Shi-chao Sun
Keyword(s):  
Structural Equation ◽  
Goodness Of Fit ◽  
Public Transit ◽  
Perceived Value ◽  
Index Score ◽  
The Public ◽  
Bus Service ◽  
Bus Services ◽  
Emotional Value

To better sustain passengers’ loyalty towards bus service, this paper addressed the modeling of the public transit loyalty by the use of structural equation model. As a novel hypothesis, the emotional value was considered to have effects on the perceived value of bus services in this study, which reflected the degree of passengers’ emotional dependence on the public transit. Specifically, in order to better assess the loyalty, seven unobserved variables were measured to construct the structural model, namely, “service guarantee,” “operational services and efficiency,” “emotional value,” “perceived value,” “expectation,” “satisfaction,” and “loyalty.” The goodness-of-fit of the model was estimated and evaluated by using the survey data harvested from Xiamen, China. Besides, the index score of variables was also computed to help determine targeted approaches to better improve the level of bus service. The results indicated that the time cost and the monetary cost actually had no effects on the perceived value of users in the case study. At the same time, however, it also proved that passengers’ emotional value towards the public transit indeed affected passengers’ perception of the service value. In addition, whether users’ perceived value was as expected determined how much passengers satisfied with the service. Regarding the index score of variables, it indicated a great dissatisfaction of passengers towards the current bus service. Unexpectedly, the score of loyalty even still retained a relatively high level, which reflected continue-to-use willingness of passengers. It implied that being subject to economic conditions and other factors, passengers were captive and had to continue relying on the public transit, in spite of their dissatisfaction. As for the improvement direction of bus services, targeted approaches should be determined to improve the quality of bus service, regarding the aspects of “condition of facilities in the bus,” “driving stability and comfort,” “vehicle speed,” and “safety.”

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