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 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 Understanding the Links between LULC Changes and SUHI in Cities: Insights from Two-Decadal Studies (2001–2020)

2021 ◽  
Vol 13 (18) ◽  
pp. 3654
Author(s):  
Ahmed Derdouri ◽  
Ruci Wang ◽  
Yuji Murayama ◽  
Toshihiro Osaragi
Keyword(s):  
Land Surface ◽  
Urban Areas ◽  
Urban Expansion ◽  
Decision Makers ◽  
Future Research ◽  
World Population ◽  
Remotely Sensed Data ◽  
Lulc Changes ◽  
Multiple Case ◽  
Urban Heat

An urban heat island (UHI) is a serious phenomenon associated with built environments and presents threats to human health. It is projected that UHI intensity will rise to record levels in the following decades due to rapid urban expansion, as two-thirds of the world population is expected to live in urban areas by 2050. Nevertheless, the last two decades have seen a considerable increase in the number of studies on surface UHI (SUHI)—a form of UHI quantified based on land surface temperature (LST) derived from satellite imagery—and its relationship with the land use/cover (LULC) changes. This surge has been facilitated by the availability of freely accessible five-decade archived remotely sensed data, the use of state-of-art analysis methods, and advancements in computing capabilities. The authors of this systematic review aimed to summarize, compare, and critically analyze multiple case studies—carried out from 2001 to 2020—in terms of various aspects: study area characteristics, data sources, methods for LULC classification and SUHI quantification, mechanisms of interaction coupled with linking techniques between SUHI intensity with LULC spatial and temporal changes, and proposed alleviation actions. The review could support decision-makers and pave the way for scholars to conduct future research, especially in vulnerable cities that have not been well studied.

scholarly journals Urban Sprawl Detection Using Remotely Sensed Data: A Case of Chennai, Tamilnadu

2017 ◽  
Author(s):  
Rajchandar Padmanaban ◽  
Pedro Cabral ◽  
Avit K. Bhowmik ◽  
Alexander Zamyatin ◽  
Oraib Almegdadi
Keyword(s):  
Ecosystem Services ◽  
Urban Planning ◽  
Urban Sprawl ◽  
Urban Areas ◽  
Agricultural Lands ◽  
Remotely Sensed Data ◽  
Spatial Metrics ◽  
The Social ◽  
Urban Settlements ◽  
Urban Extent

Urban sprawl propelled by rapid population growth leads to the shrinkage of productive agricultural lands and pristine forests in the suburban areas and, in turn, substantially alters ecosystem services. Hence, the quantification of urban sprawl is crucial for effective urban planning, and environmental and ecosystem management. Like many megacities in fast growing developing countries, Chennai, the capital of Tamilnadu and one of the business hubs in India, has experienced extensive urban sprawl triggered by the doubling of total population over the past three decades. We employed the Random Forest (RF) classification on Landsat imageries from 1991, 2003, and 2016, and computed spatial metrics to quantify the extent of urban sprawl within a 10km suburban buffer of Chennai. The rate of urban sprawl was quantified using Renyi’s entropy, and the urban extent was predicted for 2027 using land-use and land-cover change modeling. A 70.35% increase in urban areas was observed for the suburban periphery of Chennai between 1991 and 2016. The Renyi’s entropy value for year 2016 was ≥ 0.9, exhibiting a two-fold rate of urban sprawl. The spatial metrics values indicate that the existing urban areas of Chennai became denser and the suburban agricultural, forests and barren lands were transformed into fragmented urban settlements. The forecasted urban growth for 2027 predicts a conversion of 13670.33ha (16.57 % of the total landscape) of existing forests and agricultural lands into urban areas with an associated increase in the entropy value of 1.7. Our findings are relevant for urban planning and environmental management in Chennai and provide quantitative measures for addressing the social-ecological consequences of urban sprawl and the protection of ecosystem services.

scholarly journals TIME-RELATED QUALITY DIMENSIONS OF URBAN REMOTELY SENSED BIG DATA

2018 ◽  
Vol XLII-4 ◽  
pp. 315-320 ◽  
Author(s):  
Z. Kugler ◽  
G. Szabó ◽  
H. M. Abdulmuttalib ◽  
C. Batini ◽  
H. Shen ◽  
...  
Keyword(s):  
Big Data ◽  
Data Quality ◽  
Spatial Data ◽  
Urban Areas ◽  
Smart Cities ◽  
Remotely Sensed ◽  
Traffic Monitoring ◽  
Remotely Sensed Data ◽  
Related Quality ◽  
Quality Dimensions

<p><strong>Abstract.</strong> Our rapidly changing world requires new sources of image based information. The quickly changing urban areas, the maintenance and management of smart cities cannot only rely on traditional techniques based on remotely sensed data, but also new and progressive techniques must be involved. Among these technologies the volunteer based solutions are getting higher importance, like crowd-sourced image evaluations, mapping by satellite based positioning techniques or even observations done by unskilled people. Location based intelligence has become an everyday practice of our life. It is quite enough to mention the weather forecast and traffic monitoring applications, where everybody can act as an observer and acquired data – despite their heterogeneity in quality – provide great value. Such value intuitively increases when data are of better quality. In the age of visualization, real-time imaging, big data and crowd-sourced spatial data have revolutionary transformed our general applications. Most important factors of location based decisions are the time-related quality parameters of the used data. In this paper several time-related data quality dimensions and terms are defined. The paper analyses the time sensitive data characteristics of image-based crowd-sourced big data, presents quality challenges and perspectives of the users. The data quality analyses focus not only on the dimensions, but are also extended to quality related elements, metrics. The paper discusses the connection of data acquisition and processing techniques, considering even the big data aspects. The paper contains not only theoretical sections, strong practice-oriented examples on detecting quality problems are also covered. Some illustrative examples are the OpenStreetMap (OSM), where the development of urbanization and the increasing process of involving volunteers can be studied. This framework is continuing the previous activities of the Remote Sensing Data Quality Working Group (ICWGIII/IVb) of the ISPRS in the topic focusing on the temporal variety of our urban environment.</p>

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 Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

Geosciences ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 312
Author(s):  
Barbara Wiatkowska ◽  
Janusz Słodczyk ◽  
Aleksandra Stokowska
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Landsat 8 ◽  
Rate Of Increase ◽  
Management Procedures

Urban expansion is a dynamic and complex phenomenon, often involving adverse changes in land use and land cover (LULC). This paper uses satellite imagery from Landsat-5 TM, Landsat-8 OLI, Sentinel-2 MSI, and GIS technology to analyse LULC changes in 2000, 2005, 2010, 2015, and 2020. The research was carried out in Opole, the capital of the Opole Agglomeration (south-western Poland). Maps produced from supervised spectral classification of remote sensing data revealed that in 20 years, built-up areas have increased about 40%, mainly at the expense of agricultural land. Detection of changes in the spatial pattern of LULC showed that the highest average rate of increase in built-up areas occurred in the zone 3–6 km (11.7%) and above 6 km (10.4%) from the centre of Opole. The analysis of the increase of built-up land in relation to the decreasing population (SDG 11.3.1) has confirmed the ongoing process of demographic suburbanisation. The paper shows that satellite imagery and GIS can be a valuable tool for local authorities and planners to monitor the scale of urbanisation processes for the purpose of adapting space management procedures to the changing environment.

scholarly journals A multiscale assessment of Red-tailed Hawk reproductive success relative to urban density and habitat type

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Justin H White ◽  
Jessi L Brown ◽  
Zachary E Ormsby
Keyword(s):  
Urban Areas ◽  
Urban Expansion ◽  
Habitat Type ◽  
Nesting Success ◽  
Arid Environments ◽  
Hatch Date ◽  
Urban Density ◽  
Landscape Characteristics ◽  
Suburban Areas ◽  
Breeding Seasons

Abstract Despite the unique threats to wildlife in urban areas, many raptors have established successfully reproducing urban populations. To identify variations in raptor breeding ecology within an urban area, we compared metrics of Red-tailed Hawk reproductive attempts to landscape characteristics in Reno and Sparks, NV, USA during the 2015 and 2016 breeding seasons. We used the Apparent Nesting Success and logistic exposure methods to measure nesting success of the Red-tailed Hawks. We used generalized linear models to relate nesting success and fledge rate to habitat type, productivity to hatch date (Julian day) and hatch date to urban density. Nesting success was 86% and 83% for the respective years. Nesting success increased in grassland-agricultural and shrub habitats and decreased in riparian habitat within the urban landscape. Productivity was 2.23 and 2.03 per nest for the breeding seasons. Fledge rates were 72% and 77%, respectively, and decreased in riparian areas. Nestlings hatched earlier with increased urban density and earliest in suburban areas, following a negative quadratic curve. Nesting success and productivity for this population were high relative to others in North America. Productivity increased in habitats where ground prey was more accessible. We suggest that suburban areas, if not frequently disturbed, provide sufficient resources to sustain Red-tailed Hawks over extended periods. As urban expansion continues in arid environments globally, we stress that researchers monitor reproductive output across the urban predator guild to elucidate patterns in population dynamics and adaptation.

scholarly journals DaLiF: a data lifecycle framework for data-driven governments

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Syed Iftikhar Hussain Shah ◽  
Vassilios Peristeras ◽  
Ioannis Magnisalis
Keyword(s):  
Big Data ◽  
Data Management ◽  
Good Governance ◽  
Business Community ◽  
Data Driven ◽  
Governmental Organizations ◽  
Data Lifecycle ◽  
The Government ◽  
Data Ecosystem ◽  
Governance Transparency

AbstractThe public sector, private firms, business community, and civil society are generating data that is high in volume, veracity, velocity and comes from a diversity of sources. This kind of data is known as big data. Public Administrations (PAs) pursue big data as “new oil” and implement data-centric policies to transform data into knowledge, to promote good governance, transparency, innovative digital services, and citizens’ engagement in public policy. From the above, the Government Big Data Ecosystem (GBDE) emerges. Managing big data throughout its lifecycle becomes a challenging task for governmental organizations. Despite the vast interest in this ecosystem, appropriate big data management is still a challenge. This study intends to fill the above-mentioned gap by proposing a data lifecycle framework for data-driven governments. Through a Systematic Literature Review, we identified and analysed 76 data lifecycles models to propose a data lifecycle framework for data-driven governments (DaliF). In this way, we contribute to the ongoing discussion around big data management, which attracts researchers’ and practitioners’ interest.

Sign in / Sign up

Export Citation Format

Share Document