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

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 December 2020).

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; urban planning; urban data; sustainable cities
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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).

The cities have once again become the stage for a global event: the Covid-19 pandemic. In this circumstance, mapping and visualizing geographic information can be a key means of understanding the spread of the pandemic and providing a valuable insight for public health managers. This is also the context in which the Guest Editor mentions "geo-informed cities" in this introductory text to this Special Issue.

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 1400 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 (20 papers)

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Research

Article
A Cellular Automata Model for Integrated Simulation of Land Use and Transport Interactions
ISPRS Int. J. Geo-Inf. 2021, 10(3), 149; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10030149 - 08 Mar 2021
Viewed by 432
Abstract
Cellular automata (CA) models have been used in urban studies for dealing with land use change. Transport and accessibility are arguably the main drivers of urban change and have a direct influence on land use. Land use and transport interaction models deal with [...] Read more.
Cellular automata (CA) models have been used in urban studies for dealing with land use change. Transport and accessibility are arguably the main drivers of urban change and have a direct influence on land use. Land use and transport interaction models deal with the complexity of this relationship using many different approaches. CA models incorporate these drivers, but usually consider transport (and accessibility) variables as exogenous. Our paper presents a CA model where transport variables are endogenous to the model and are calibrated along with the land use variables to capture the interdependent complexity of these phenomena. The model uses irregular cells and a variable neighborhood to simulate land use change, taking into account the effect of the road network. Calibration is performed through a particle swarm algorithm. We present an application of the model to a comparison of scenarios for the construction of a ring road in the city of Coimbra, Portugal. The results show the ability of the CA model to capture the influence of change of the transport network (and thus in accessibility) in the land use dynamics. Full article
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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Article
Estimating the Impacts of Proximity to Public Transportation on Residential Property Values: An Empirical Analysis for Hartford and Stamford Areas, Connecticut
ISPRS Int. J. Geo-Inf. 2021, 10(2), 44; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10020044 - 20 Jan 2021
Viewed by 567
Abstract
Public transit infrastructure may increase residential property values by improving accessibility and reducing commute expenses in urban areas. Prior studies have investigated the impacts of the proximity to public transportation on property values and obtained mixed conclusions. Many of these studies were focused [...] Read more.
Public transit infrastructure may increase residential property values by improving accessibility and reducing commute expenses in urban areas. Prior studies have investigated the impacts of the proximity to public transportation on property values and obtained mixed conclusions. Many of these studies were focused on one transit mode for a single city. In this study, a hedonic pricing model is constructed to investigate the impacts of commuter rail/Bus Rapid Transit (BRT) and bus lines separately in two different areas: the Stamford area (Stamford–Darien–New Canaan) and the Hartford area (Hartford–West Hartford–East Hartford), Connecticut. Comparison of the results from Ordinary Least Square and Geographically Weighted Regression (GWR) indicates that estimation accuracy can be improved by considering local variation. Results from GWR show that impacts of proximity to bus and rail/BRT on property values vary spatially in the Hartford area. Negative impacts of bus stops are found in downtown Hartford and positive impacts in the west and east sides of Hartford. Impacts from rail/BRT are relatively minor compared with bus lines, partly due to the relatively recent launching of the BRT and Hartford rail line. In contrast, most properties in the Stamford area show appreciation towards rail service and depreciation to bus service. This study reveals the roles of different public transit systems in affecting residential property values. It also provides empirical evidence for future transit-oriented development in this region for uplifting the real estate market. Full article
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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Article
Mapping Food and Health Premises in Barcelona. An Approach to Logics of Distribution and Proximity of Essential Urban Services
ISPRS Int. J. Geo-Inf. 2020, 9(12), 746; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120746 - 13 Dec 2020
Viewed by 639
Abstract
The research analyzes the image of Barcelona and compares differences in quantity, variety and proximity of some essential services in diverse urban fragments. Focusing on food and health premises as critical universal services, series of maps provide overviews on the intensity of use [...] Read more.
The research analyzes the image of Barcelona and compares differences in quantity, variety and proximity of some essential services in diverse urban fragments. Focusing on food and health premises as critical universal services, series of maps provide overviews on the intensity of use to which each service is subjected, latent logics of their physical proximity and performance in regular urban fabrics due to the combination of activities and population distribution. The research uses a methodological approach and parameterization of the minimum daily urban mixture to highlight the uniqueness of the case of Barcelona, distinguished by the compactness of the urban fabric and the contiguity of activities, and to describe an extensive characterization of areas that from this perspective can be considered hyper-served or under-served. This investigation aims to contribute to the understanding of the necessity of the urban mixture and to provide clues about the distribution of services and activities. Full article
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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Article
Urban Population Distribution Mapping with Multisource Geospatial Data Based on Zonal Strategy
ISPRS Int. J. Geo-Inf. 2020, 9(11), 654; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110654 - 30 Oct 2020
Viewed by 563
Abstract
Mapping population distribution at fine resolutions with high accuracy is crucial to urban planning and management. This paper takes Guangzhou city as the study area, illustrates the gridded population distribution map by using machine learning methods based on zoning strategy with multisource geospatial [...] Read more.
Mapping population distribution at fine resolutions with high accuracy is crucial to urban planning and management. This paper takes Guangzhou city as the study area, illustrates the gridded population distribution map by using machine learning methods based on zoning strategy with multisource geospatial data such as night light remote sensing data, point of interest data, land use data, and so on. The street-level accuracy evaluation results show that the proposed approach achieved good overall accuracy, with determinant coefficient (R2) being 0.713 and root mean square error (RMSE) being 5512.9. Meanwhile, the goodness of fit for single linear regression (LR) model and random forest (RF) regression model are 0.0039 and 0.605, respectively. For dense area, the accuracy of the random forest model is better than the linear regression model, while for sparse area, the accuracy of the linear regression model is better than the random forest model. The results indicated that the proposed method has great potential in fine-scale population mapping. Therefore, it is advised that the zonal modeling strategy should be the primary choice for solving regional differences in the population distribution mapping research. Full article
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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Article
What Is Urban after All? A Critical Review of Measuring and Mapping Urban Typologies in Portugal
ISPRS Int. J. Geo-Inf. 2020, 9(11), 630; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110630 - 26 Oct 2020
Viewed by 778
Abstract
The concept of urban area is complex and has been discussed for many years by several authors and organisations through different perspectives and methodological approaches. For administrative and comparison purposes statistical institutions, both at the national and international levels, classify territories according to [...] Read more.
The concept of urban area is complex and has been discussed for many years by several authors and organisations through different perspectives and methodological approaches. For administrative and comparison purposes statistical institutions, both at the national and international levels, classify territories according to a certain degree of urbanisation defining typologies from which indicators and certain public policies are applied. The purpose of this study is to discuss the relevance and suitability of different urban typologies. Through mapping and measuring the data of official documents, the urban dimension of Mainland Portuguese territory is discussed and its usefulness concerning the allocation of resources for promoting territorial cohesion is stressed. Results show the inadequacy of these classifications to inform planning actions, decision making, and to promote territorial policies. It also provides evidence of inaccuracies that distort the reading of the territorial reality of the case study. Full article
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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Article
A 3D Geodatabase for Urban Underground Infrastructures: Implementation and Application to Groundwater Management in Milan Metropolitan Area
ISPRS Int. J. Geo-Inf. 2020, 9(10), 609; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100609 - 21 Oct 2020
Cited by 1 | Viewed by 1191
Abstract
The recent rapid increase in urbanization has led to the inclusion of underground spaces in urban planning policies. Among the main subsurface resources, a strong interaction between underground infrastructures and groundwater has emerged in many urban areas in the last few decades. Thus, [...] Read more.
The recent rapid increase in urbanization has led to the inclusion of underground spaces in urban planning policies. Among the main subsurface resources, a strong interaction between underground infrastructures and groundwater has emerged in many urban areas in the last few decades. Thus, listing the underground infrastructures is necessary to structure an urban conceptual model for groundwater management needs. Starting from a municipal cartography (Open Data), thus making the procedure replicable, a GIS methodology was proposed to gather all the underground infrastructures into an updatable 3D geodatabase (GDB) for the metropolitan city of Milan (Northern Italy). The underground volumes occupied by three categories of infrastructures were included in the GDB: (a) private car parks, (b) public car parks and (c) subway lines and stations. The application of the GDB allowed estimating the volumes lying below groundwater table in four periods, detected as groundwater minimums or maximums from the piezometric trend reconstructions. Due to groundwater rising or local hydrogeological conditions, the shallowest, non-waterproofed underground infrastructures were flooded in some periods considered. This was evaluated in a specific pilot area and qualitatively confirmed by local press and photographic documentation reviews. The methodology emerged as efficient for urban planning, particularly for urban conceptual models and groundwater management plans definition. Full article
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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Article
Accurate Road Marking Detection from Noisy Point Clouds Acquired by Low-Cost Mobile LiDAR Systems
ISPRS Int. J. Geo-Inf. 2020, 9(10), 608; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100608 - 20 Oct 2020
Cited by 2 | Viewed by 780
Abstract
Road markings that provide instructions for unmanned driving are important elements in high-precision maps. In road information collection technology, multi-beam mobile LiDAR scanning (MLS) is currently adopted instead of traditional mono-beam LiDAR scanning because of the advantages of low cost and multiple fields [...] Read more.
Road markings that provide instructions for unmanned driving are important elements in high-precision maps. In road information collection technology, multi-beam mobile LiDAR scanning (MLS) is currently adopted instead of traditional mono-beam LiDAR scanning because of the advantages of low cost and multiple fields of view for multi-beam laser scanners; however, the intensity information scanned by multi-beam systems is noisy and current methods designed for road marking detection from mono-beam point clouds are of low accuracy. This paper presents an accurate algorithm for detecting road markings from noisy point clouds, where most nonroad points are removed and the remaining points are organized into a set of consecutive pseudo-scan lines for parallel and/or online processing. The road surface is precisely extracted by a moving fitting window filter from each pseudo-scan line, and a marker edge detector combining an intensity gradient with an intensity statistics histogram is presented for road marking detection. Quantitative results indicate that the proposed method achieves average recall, precision, and Matthews correlation coefficient (MCC) levels of 90%, 95%, and 92%, respectively, showing excellent performance for road marking detection from multi-beam scanning point clouds. Full article
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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Article
A Smooth Transition Algorithm for Adjacent Panoramic Viewpoints Using Matched Delaunay Triangular Patches
ISPRS Int. J. Geo-Inf. 2020, 9(10), 596; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100596 - 10 Oct 2020
Viewed by 779
Abstract
The unnatural panoramic image transition between two adjacent viewpoints reduces the immersion and interactive experiences of 360° panoramic walkthrough systems. In this paper, a dynamic panoramic image rendering and smooth transition algorithm for adjacent viewpoints is proposed. First, the feature points of adjacent [...] Read more.
The unnatural panoramic image transition between two adjacent viewpoints reduces the immersion and interactive experiences of 360° panoramic walkthrough systems. In this paper, a dynamic panoramic image rendering and smooth transition algorithm for adjacent viewpoints is proposed. First, the feature points of adjacent view images are extracted, a robust matching algorithm is used to establish adjacent point pairs, and the matching triangles are formed by using the homonymous points. Then, a dynamic transition model is formed by the simultaneous linear transitions of shape and texture for each control triangle. Finally, the smooth transition between adjacent viewpoints is implemented by overlaying the dynamic transition model with the 360° panoramic walkthrough scene. Experimental results show that this method has obvious advantages in visual representation with distinct visual movement. It can realize the smooth transition between two indoor panoramic stations with arbitrary station spacing, and its execution efficiency is up to 50 frames per second. It effectively enhances the interactivity and immersion of 360° panoramic walkthrough systems. Full article
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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Article
Evaluation of the Space Syntax Measures Affecting Pedestrian Density through Ordinal Logistic Regression Analysis
ISPRS Int. J. Geo-Inf. 2020, 9(10), 589; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100589 - 07 Oct 2020
Viewed by 803
Abstract
This paper examines the relationship between pedestrian density and space syntax measures in a university campus using ordinal logistic regression analysis. The pedestrian density assumed as the dependent variable of regression analysis was categorised in low, medium, and high classes by using Jenks [...] Read more.
This paper examines the relationship between pedestrian density and space syntax measures in a university campus using ordinal logistic regression analysis. The pedestrian density assumed as the dependent variable of regression analysis was categorised in low, medium, and high classes by using Jenks natural break classification. The data elements of groups were derived from pedestrian counts performed in 22 gates 132 times. The counting period grouped in nominal categories was assumed as an independent variable. Another independent was one of the 15 derived measures of axial analysis and visual graphic analysis. The statistically significant model results indicated that the integration of axial analysis was the most reasonable measure that explained the pedestrian density. Then, the changes in integration values of current and master plan datasets were analysed using paired sample t-test. The calculated p-value of t-test proved that the master plan would change the campus morphology for pedestrians. Full article
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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Graphical abstract

Article
Identification and Geographic Distribution of Accommodation and Catering Centers
by and
ISPRS Int. J. Geo-Inf. 2020, 9(9), 546; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090546 - 14 Sep 2020
Viewed by 736
Abstract
As the most important manifestation of the activities of the life service industry, the reasonable layout of spatial agglomeration and dispersion of the accommodation and catering industry plays an important role in guiding the spatial structure of the urban industry and population. Applying [...] Read more.
As the most important manifestation of the activities of the life service industry, the reasonable layout of spatial agglomeration and dispersion of the accommodation and catering industry plays an important role in guiding the spatial structure of the urban industry and population. Applying the contour tree and location quotient index methods, based on points of interest (POI) data of the accommodation and catering industry in Beijing and on the identification of the spatial structure and cluster center of the accommodation and catering industry, we investigated the distribution and agglomeration characteristics of the urban accommodation and catering industry from the perspective of industrial spatial differentiation. The results show that: (1) the accommodation and catering industry in Beijing presents a polycentric agglomeration pattern in space, mainly distributed within a radius of 20 km from the city center and on a relatively large scale; areas beyond this distance contain isolated single cluster centers. (2) From the perspective of the industry, the cluster centers close to the core area of the city are characterized by the agglomeration of multiple advantageous industries, while those in the outer suburbs of the city are more prominent in a single industry. (3) From the perspective of the location quotient of cluster centers, the leisure catering industries are mainly located close to the urban centers. On the contrary, the cluster centers in the outer suburbs and counties are relatively small and dominated by restaurants and fast food industries. Commercial accommodation businesses are mainly distributed in the transportation hub centers and in entertainment and leisure areas. Full article
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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Article
Urban Green Plastic Cover Mapping Based on VHR Remote Sensing Images and a Deep Semi-Supervised Learning Framework
ISPRS Int. J. Geo-Inf. 2020, 9(9), 527; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090527 - 02 Sep 2020
Cited by 1 | Viewed by 973
Abstract
With the rapid process of both urban sprawl and urban renewal, large numbers of old buildings have been demolished in China, leading to wide spread construction sites, which could cause severe dust contamination. To alleviate the accompanied dust pollution, green plastic mulch has [...] Read more.
With the rapid process of both urban sprawl and urban renewal, large numbers of old buildings have been demolished in China, leading to wide spread construction sites, which could cause severe dust contamination. To alleviate the accompanied dust pollution, green plastic mulch has been widely used by local governments of China. Therefore, timely and accurate mapping of urban green plastic covered regions is of great significance to both urban environmental management and the understanding of urban growth status. However, the complex spatial patterns of the urban landscape make it challenging to accurately identify these areas of green plastic cover. To tackle this issue, we propose a deep semi-supervised learning framework for green plastic cover mapping using very high resolution (VHR) remote sensing imagery. Specifically, a multi-scale deformable convolution neural network (CNN) was exploited to learn representative and discriminative features under complex urban landscapes. Afterwards, a semi-supervised learning strategy was proposed to integrate the limited labeled data and massive unlabeled data for model co-training. Experimental results indicate that the proposed method could accurately identify green plastic-covered regions in Jinan with an overall accuracy (OA) of 91.63%. An ablation study indicated that, compared with supervised learning, the semi-supervised learning strategy in this study could increase the OA by 6.38%. Moreover, the multi-scale deformable CNN outperforms several classic CNN models in the computer vision field. The proposed method is the first attempt to map urban green plastic-covered regions based on deep learning, which could serve as a baseline and useful reference for future research. Full article
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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Article
Mapping the Catchment Area of Park and Ride Facilities within Urban Environments
ISPRS Int. J. Geo-Inf. 2020, 9(9), 501; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090501 - 21 Aug 2020
Cited by 6 | Viewed by 1029
Abstract
A Park and Ride (P & R) system is a set of facilities located throughout an urban area that can serve as transfer points for travelers that would like to utilize their private vehicles for one part of their journey and a more [...] Read more.
A Park and Ride (P & R) system is a set of facilities located throughout an urban area that can serve as transfer points for travelers that would like to utilize their private vehicles for one part of their journey and a more sustainable transport mode, such as public transport, for another part of the same journey. The catchment area of the facilities is identified as a fundamental element for planning a P & R system. It can be assumed to be accurately represented by several geometric shapes, such as a circle or a parabola. In that regard, a method denominated as the parabola method can be used to visualize those geometric shapes on digital maps of an urban environment. It can be implemented as a software program that integrates the variables that represent the elements of the P & R system as well as the set of equations that are used in a geographic information system (GIS) software. A significant aspect of how the parabola method is applied is its orientation as a shape, which is traditionally configured in respect to the area of major business activity or central business districts (CBDs). In fact, the research presented in this article aims to provide a new approach to the parabola’s orientation to study the P & R system’s catchment area by proposing the parabola’s orientation according to the primary access that potential users used to reach the facility. A case study that portrays the application of our method is given that is focused on the medium-sized city of Cuenca, Ecuador, where we determine which approach to the parabola’s orientation is the most suitable. In conclusion, the second approach proposed in this research reflects in a more realistic form the operation of the catchment area of the P & R system, considering a better distribution of the coverage area of the P & R system in the urban environment. Full article
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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Article
Generating and Mapping Amazonian Urban Regions Using a Geospatial Approach
ISPRS Int. J. Geo-Inf. 2020, 9(7), 453; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9070453 - 17 Jul 2020
Cited by 2 | Viewed by 1491
Abstract
(1) background: Urban representations of the Amazon are urgently needed in order to better understand the complexity of urban processes in this area of the World. So far, limited work that represents Amazonian urban regions has been carried out. (2) methods: Our study [...] Read more.
(1) background: Urban representations of the Amazon are urgently needed in order to better understand the complexity of urban processes in this area of the World. So far, limited work that represents Amazonian urban regions has been carried out. (2) methods: Our study area is the Ecuadorian Amazon. We performed a K-means algorithm using six urban indicators: Urban fractal dimension, number of paved streets, urban radiant intensity (luminosity), and distances to the closest new deforested areas, to oil pollution sources, and to mining pollution sources. We also carried out fieldwork to qualitatively validate our geospatial and statistical analyses. (3) results: We generated six Amazonian urban regions representing different urban configurations and processes of major cities, small cities, and emerging urban zones. The Amazonian urban regions generated represent the urban systems of the Ecuadorian Amazon at a general scale, and correspond to the urban realities at a local scale. (4) conclusions: An Amazonian urban region is understood as a set of urban zones that are dispersed and share common urban characteristics such a similar distance to oil pollution sources or similar urban radiant intensity. Our regionalization model represents the complexity of the Amazonian urban systems, and the applied methodology could be transferred to other Amazonian countries. Full article
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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Article
Measuring Accessibility to Various ASFs from Public Transit using Spatial Distance Measures in Indian Cities
ISPRS Int. J. Geo-Inf. 2020, 9(7), 446; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9070446 - 17 Jul 2020
Cited by 3 | Viewed by 895
Abstract
Nowadays, accessibility to facilities is one of the most discussed issues in sustainable urban planning. In the current research, two spatial distance accessibility measures were applied to evaluate the accessibility to amenities, services, and facilities (ASFs) from public transit (PT) by walking distance [...] Read more.
Nowadays, accessibility to facilities is one of the most discussed issues in sustainable urban planning. In the current research, two spatial distance accessibility measures were applied to evaluate the accessibility to amenities, services, and facilities (ASFs) from public transit (PT) by walking distance in six Indian cities. The first stage accounts for distance measures using the Euclidean distance with a new methodical approach derived from the built-up area with a spatial resolution of 30 m from Landsat data, and for the network distance method, the actual road distances using OpenStreetMap (OSM) for different threshold ranges of distances were derived. Meanwhile, in the second stage, indicators such as built-up area, network connectivity, and network density with the percentage of ASFs are evaluated and combined for normalization process for ranking the city. The present study assesses the accessibility to various ASFs from PT at city level and explores whether the actual road network access (by measuring distance) in Indian cities is contributing to a high level of accessibility. It adopts a unique approach using statistical tools while assessing both Euclidean and network distances. It models a framework for overall benchmarking in all six cities by ranking them for their accessibility. The results show various scenarios in terms of the rank of cities, which had been strongly affected by distance metrics (Euclidean vs. network) and thus emphasize the careful use of these measures as supporting tools for planning. This facilitates the identification of the local barriers and problems with network access that affect the actual distance. This unique approach can help policymakers to identify the gaps in PT coverage for reaching ASFs. Furthermore, it helps in crucial implementation by strategic planning that can be achieved using these distance criteria. Full article
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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Article
Measuring Accessibility of Healthcare Facilities for Populations with Multiple Transportation Modes Considering Residential Transportation Mode Choice
ISPRS Int. J. Geo-Inf. 2020, 9(6), 394; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9060394 - 16 Jun 2020
Cited by 3 | Viewed by 1179
Abstract
Accessibility research of healthcare facilities is developing towards multiple transportation modes (MTM), which are influenced by residential transportation choices and preferences. Due to differences in travel impact factors such as traffic conditions, origin location, distance to the destination, and economic cost, residents’ daily [...] Read more.
Accessibility research of healthcare facilities is developing towards multiple transportation modes (MTM), which are influenced by residential transportation choices and preferences. Due to differences in travel impact factors such as traffic conditions, origin location, distance to the destination, and economic cost, residents’ daily travel presents different residential transportation mode choices (RTMC). The purpose of our study was to measure the spatial accessibility of healthcare facilities based on MTM considering RTMC (MTM-RTMC). We selected the gravity two-step floating catchment area method (G2SFCA) as a fundamental model. Through the single transportation mode (STM), MTM, and MTM-RTMC, three aspects used to illustrate and redesign the G2SFCA, we obtained the MTM-RTMC G2SFCA model that integrates RTMC probabilities and the travel friction coefficient. We selected Nanjing as the experimental area, used route planning data of four modes (including driving, walking, public transportation, and bicycling) from a web mapping platform, and applied the three models to pediatric clinic services to measure accessibility. The results show that the MTM-RTMC mechanism is to make up for the traditional estimation of accessibility, which loses sight of the influence of residential transportation choices. The MTM-RTMC mechanism that provides a more realistic and reliable way can generalize to major accessibility models and offers preferable guidance for policymakers. Full article
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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Article
Modelling Housing Rents Using Spatial Autoregressive Geographically Weighted Regression: A Case Study in Cracow, Poland
ISPRS Int. J. Geo-Inf. 2020, 9(6), 346; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9060346 - 26 May 2020
Cited by 4 | Viewed by 1391
Abstract
The proportion of tenants will undoubtedly rise in Poland, where at present, the ownership housing model is very dominant. As a result, the rental housing market in Poland is currently under-researched in comparison with owner-occupancy. In order to narrow this research gap, this [...] Read more.
The proportion of tenants will undoubtedly rise in Poland, where at present, the ownership housing model is very dominant. As a result, the rental housing market in Poland is currently under-researched in comparison with owner-occupancy. In order to narrow this research gap, this study attempts to identify the determinants affecting rental prices in Cracow. The latter were obtained from the internet platform otodom.pl using the web scraping technique. To identify rent determinants, ordinary least squares (OLS) regression and spatial econometric methods were used. In particular, traditional spatial autoregressive model (SAR) and spatial autoregressive geographically weighted regression (GWR-SAR) were employed, which made it possible to take into account the spatial heterogeneity of the parameters of determinants and the spatially changing spatial autocorrelation of housing rents. In-depth analysis of rent determinants using the GWR-SAR model exposed the complexity of the rental market in Cracow. Estimates of the above model revealed that many local markets can be identified in Cracow, with different factors shaping housing rents. However, one can identify some determinants that are ubiquitous for almost the entire city. This concerns mainly the variables describing the area of the flat and the age of the building. Moreover, the Monte Carlo test indicated that the spatial autoregressive parameter also changes significantly over space. Full article
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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Article
Visualization, Spatiotemporal Patterns, and Directional Analysis of Urban Activities Using Geolocation Data Extracted from LBSN
ISPRS Int. J. Geo-Inf. 2020, 9(2), 137; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9020137 - 24 Feb 2020
Cited by 4 | Viewed by 1494
Abstract
Location-based social networks (LBSNs) have rapidly prevailed in China with the increase in smart devices use, which has provided a wide range of opportunities to analyze urban behavior in terms of the use of LBSNs. In a LBSN, users socialize by sharing their [...] Read more.
Location-based social networks (LBSNs) have rapidly prevailed in China with the increase in smart devices use, which has provided a wide range of opportunities to analyze urban behavior in terms of the use of LBSNs. In a LBSN, users socialize by sharing their location (also referred to as “geolocation”) in the form of a tweet (also referred to as a “check-in”), which contains information in the form of, but is not limited to, text, audio, video, etc., which records the visited place, movement patterns, and activities performed (e.g., eating, living, working, or leisure). Understanding the user’s activities and behavior in space and time using LBSN datasets can be achieved by archiving the daily activities, movement patterns, and social media behavior patterns, thus representing the user’s daily routine. The current research observing and analyzing urban activities behavior was often supported by the volunteered sharing of geolocation and the activity performed in space and time. The objective of this research was to observe the spatiotemporal and directional trends and the distribution differences of urban activities at the city and district levels using LBSN data. The density was estimated, and the spatiotemporal trend of activities was observed, using kernel density estimation (KDE); for spatial regression analysis, geographically weighted regression (GWR) analysis was used to observe the relationship between different activities in the study area. Finally, for the directional analysis, to observe the principle orientation and direction, and the spatiotemporal movement and extension trends, a standard deviational ellipse (SDE) analysis was used. The results of the study show that women were more inclined to use social media compared with men. However, the activities of male users were different during weekdays and weekends compared to those of female users. The results of the directional analysis at the district level reflect the change in the trajectory and spatiotemporal dynamics of activities. The directional analysis at the district level reveals its fine spatial structure in comparison to the whole city level. Therefore, LBSN can be considered as a supplementary and reliable source of social media big data for observing urban activities and behavior within a city in space and time. Full article
(This article belongs to the Special Issue Measuring, Mapping, Modeling, and Visualization of Cities)
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Article
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://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9020092 - 01 Feb 2020
Cited by 5 | Viewed by 1425
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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Article
Towards Increasing Residential Market Transparency: Mapping Local Housing Prices and Dynamics
ISPRS Int. J. Geo-Inf. 2020, 9(1), 2; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9010002 - 18 Dec 2019
Cited by 7 | Viewed by 1063
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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Article
Visualization of Pedestrian Density Dynamics Using Data Extracted from Public Webcams
ISPRS Int. J. Geo-Inf. 2019, 8(12), 559; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8120559 - 05 Dec 2019
Cited by 4 | Viewed by 1194
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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