Special Issue "Measuring, Mapping, Modeling, and Visualization of Cities"

Special Issue Editor

Prof. Dr. José António Tenedório
E-Mail Website
Guest Editor
NOVA School of Social Sciences and Humanities, Interdisciplinary Centre of Social Sciences (CICS.NOVA), Universidade NOVA de Lisboa, Av. de Berna, 26-C, 1069-061 Lisboa, Portugal
Interests: Geography; GIScience; Remote Sensing; Spatial Analysis; Geographical Modeling; Urban Data; Urban Planning; Sustainable Urbanism; Sustainable cities

Special Issue Information

Dear Colleagues,

Geo-Information is a crucial basis for understanding the functioning of the cities and urban environments. The scientific knowledge about these environments is essential for decision making regarding urban planning, sustainable urbanism, and public decision making.

This Special Issue is dedicated to measuring, mapping, modeling, and visualization of cities and urban environments. The in-depth knowledge of urban environments and cities depends heavily on building evidence based on geo-information. This construction of evidence of functioning (forms, flows, trends, rhythms, intensities, systems, hierarchies, etc.) and of urban change (in urban functions, in the virtualization of commerce and services, in the use of public space, urban thermal comfort, etc.) depends of the quality of geographical data. Public policies (consideration of environmental and urban risks, soft mobility, alternative energies, sustainability, circular economy, among other policies) should be based on measurement (spatial data acquisition), mapping (spatialization data), modeling the current situation, and simulating future situations using intensive visualization, including virtual visualization.

Currently, we can talk about geo-informed cities. That is, cities that can be represented (measured, mapped, modeled, and visualized) with data resulting from public services (namely, the statistical services of each country, region or city), but also through data shared on social networks and the internet, and acquired by human sensing (using mobile phone, computer, Bluetooth, Wi-Fi, and other technologies).

I invite you to participate in the construction of this representation of the cities and urban environments based on measuring, mapping, modeling and visualization, relating (but not limited) to the following topics:

- Measuring using imagery (satellite imagery, UAV imagery, LiDAR, others), GPS/GLONASS technology, geolocation data, Big Data, navigation, tracking, social networking, gaming, etc.;

- Mapping with GIS for geographical analysis, spatial thinking, spatial reasoning, and spatial behavior;

- Modelling data (2D/3D space, time and scale dimensions) and cities (models for recognition of patterns, processes, equilibrium, dynamics, flows, networks, evolution and emergence, city-games, spatial cognition, etc.);

- Visualization of urban geographic representations (virtual cities and geo-information, map animation, data sharing, mobile devices, augmented reality, virtual reality, emerging technologies, tools and applications).

Prof. Dr. José António Tenedório

Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Geo-informed cities
  • Urban data
  • Urban remote sensing
  • Urban mapping
  • Urban modelling
  • Urban visualization
  • Virtual cities
  • Urban models
  • Virtual public spaces
  • Geospatial big data computing

Published Papers (3 papers)

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Research

Open AccessArticle
Mapping Urban Spatial Structure Based on POI (Point of Interest) Data: A Case Study of the Central City of Lanzhou, China
ISPRS Int. J. Geo-Inf. 2020, 9(2), 92; https://doi.org/10.3390/ijgi9020092 - 01 Feb 2020
Abstract
The study of urban spatial structure is currently one of the most popular research fields in urban geography. This study uses Lanzhou, one of the major cities in Northwest China, as a case area. Using the industry classification of POI data, the nearest-neighbor [...] Read more.
The study of urban spatial structure is currently one of the most popular research fields in urban geography. This study uses Lanzhou, one of the major cities in Northwest China, as a case area. Using the industry classification of POI data, the nearest-neighbor index, kernel density estimation, and location entropy are adopted to analyze the spatial clustering-discrete distribution characteristics of the overall economic geographical elements of the city center, the spatial distribution characteristics of the various industry elements, and the overall spatial structure characteristics of the city. All of these can provide a scientific reference for the sustainable optimization of urban space. The urban economic geographical elements generally present the distribution trend of center agglomeration. In respect of spatial distribution, the economic geographical elements in the central urban area of Lanzhou have obvious characteristics of central agglomeration. Many industrial elements have large-scale agglomeration centers, which have formed specialized functional areas. There is a clear “central–peripheral” difference distribution in space, with an obvious circular structure. Generally, tertiary industry is distributed in the central area, and secondary industry is distributed in the peripheral areas. In general, a strip-shaped urban spatial structure with a strong main center, weak subcenter and multiple groups is present. Improving the complexity of urban functional space is an important goal of spatial structure optimization. Full article
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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Open AccessArticle
Towards Increasing Residential Market Transparency: Mapping Local Housing Prices and Dynamics
ISPRS Int. J. Geo-Inf. 2020, 9(1), 2; https://doi.org/10.3390/ijgi9010002 - 18 Dec 2019
Abstract
This article attempts to use spatial maps as a way of presenting additional information about the phenomena occurring in the housing market. In our opinion, spatial maps may facilitate understanding and provide more detailed information, which undoubtedly should increase the transparency of the [...] Read more.
This article attempts to use spatial maps as a way of presenting additional information about the phenomena occurring in the housing market. In our opinion, spatial maps may facilitate understanding and provide more detailed information, which undoubtedly should increase the transparency of the housing market. The study used 12,219 transactions of apartments in Poznań in the years 2013–2017. General principles of price visualization activity and housing market dynamics were established in this study. The map of prices may reflect the location values determined by the quality of the urban infrastructure, distance from specific locations, and environmental factors. Market activity maps reveal areas where the market is dynamically developing, while information on trends in the number of transactions and price changes may demonstrate the growing or declining attractiveness of areas. The research is based on a model of hedonic regression in the form of ordinary least squares (OLS), quantile regression (QR), and geographically weighted regression (GWR). The maps presented should increase the transparency of the residential market (e.g., by providing more detailed information). However, one should bear in mind the limitations in the use of these methods resulting from a small number of transactions in a thin market. Full article
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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Open AccessArticle
Visualization of Pedestrian Density Dynamics Using Data Extracted from Public Webcams
ISPRS Int. J. Geo-Inf. 2019, 8(12), 559; https://doi.org/10.3390/ijgi8120559 - 05 Dec 2019
Abstract
Accurate information on the number and distribution of pedestrians in space and time helps urban planners maintain current city infrastructure and design better public spaces for local residents and visitors. Previous studies have demonstrated that using webcams together with crowdsourcing platforms to locate [...] Read more.
Accurate information on the number and distribution of pedestrians in space and time helps urban planners maintain current city infrastructure and design better public spaces for local residents and visitors. Previous studies have demonstrated that using webcams together with crowdsourcing platforms to locate pedestrians in the captured images is a promising technique for analyzing pedestrian activity. However, it is challenging to efficiently transform the time series of pedestrian locations in the images to information suitable for geospatial analytics, as well as visualize data in a meaningful way to inform urban design or decision making. In this study, we propose to use a space-time cube (STC) representation of pedestrian data to analyze the spatio-temporal patterns of pedestrians in public spaces. We take advantage of AMOS (The Archive of Many Outdoor Scenes), a large database of images captured by thousands of publicly available, outdoor webcams. We developed a method to obtain georeferenced spatio-temporal data from webcams and to transform them into high-resolution continuous representation of pedestrian densities by combining bivariate kernel density estimation with trivariate, spatio-temporal spline interpolation. We demonstrate our method on two case studies analyzing pedestrian activity of two city plazas. The first case study explores daily and weekly spatio-temporal patterns of pedestrian activity while the second one highlights the differences in pattern before and after plaza’s redevelopment. While STC has already been used to visualize urban dynamics, this is the first study analyzing the evolution of pedestrian density based on crowdsourced time series of pedestrian occurrences captured by webcam images. Full article
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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