Special Issue "Geodata Science and Spatial Analysis in Urban Studies"

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

Deadline for manuscript submissions: closed (30 April 2021).

Special Issue Editors

Prof. Dr. Maria Antonia Brovelli
E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
Interests: geospatial web; geodata science; citizen science; open science; open data; open geospatial software; geospatial artificial intelligence
Special Issues and Collections in MDPI journals
Prof. Dr. Xiao-guang Zhou
E-Mail Website
Guest Editor
School of Geosciences and Info-Physics, Central South University, Changsha 410083, Hunan, China
Interests: spatiotemporal data model; incremental update; topological relationship; change detection; crowdsourcing data; data integration
Special Issues and Collections in MDPI journals
Dr. Hussein Abdulmuttalib
E-Mail Website
Guest Editor
GIS Center, Dubai Municipality, POBox 67, Dubai UAE
Interests: geospatial data quality; spatial environmental analysis; geo-big data analysis; algorithms and interpolation of 3d surfaces; openness; urban smart cities; OBIA
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Today, more than half of humanity lives in urban settlements and this trend continues to grow. Urban—and "big urban"—settlements are evidence of the self-shaping nature of human civilizations due to political, economic, social, and spiritual reasons. The great power of urban convergence is conducive to promoting industrial development, scientific and technological progress, and cultural exchanges. However, as inequality in the distribution of wealth and extreme gaps in living conditions become evident, social problems and conflicts arise leading to serious consequences. Systems such as transportation, housing, employment, social order, security, privacy, and public morality are facing tremendous pressure and challenges. Therefore, positioning the research agenda for urban studies becomes an urgent matter.

In the past decade, geodata science and spatial analysis achieved significant development thanks to the increased availability of remote sensing imagery, sensors and Internet of Things (IoT) data, crowdsourced geospatial data, and new methods that appeared or gained renewed importance; like artificial intelligence (AI), specifically in its application to earth observation (EO) and geospatial data. These resources provide opportunities to model and analyze the phenomenon in urban areas.

This Special Issue focuses on Geodata Science and Spatial Analysis in Urban Studies. Topics of interest for this issue include but are not limited to significant improvements in modeling and representation of urban geo big data, collection and evaluation of urban reliable information, geo-computing algorithms, and new methods for urban studies, both analytical and quantitative applied to urban issues.

Prof. Dr. Maria Antonia Brovelli
Prof. Dr. Xiao-guang Zhou
Dr. Hussein Abdulmuttalib
Guest Editors

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

  • urban studies, smart cities, and communities
  • urban geo big data, geo-visualization of the city environment
  • geo-computation, spatial analytics, and artificial intelligence applied to the urban context.

Published Papers (26 papers)

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Research

Article
Understanding the Spatial Effects of Unaffordable Housing Using the Commuting Patterns of Workers in the New Zealand Integrated Data Infrastructure
ISPRS Int. J. Geo-Inf. 2021, 10(7), 457; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10070457 - 02 Jul 2021
Viewed by 573
Abstract
Commuting behaviour has been intensively examined by geographers, urban planners, and transportation researchers, but little is known about how commuting behaviour is spatially linked with the job and housing markets in urban cities. New Zealand has been recognised as one of the countries [...] Read more.
Commuting behaviour has been intensively examined by geographers, urban planners, and transportation researchers, but little is known about how commuting behaviour is spatially linked with the job and housing markets in urban cities. New Zealand has been recognised as one of the countries having the most unaffordable housing over the past decade. A group of middle-class professionals called ‘key workers’, also known during the pandemic as ‘essential workers’, provide essential services for the community, but cannot afford to live near their workplaces due to a lack of affordable housing. As a result, these key workers incur significant sub-optimal commuting. Such job-housing imbalance has contributed to a so-called spatial mismatch problem. This study aims to visualise the excess commuting patterns of individual workers using the Integrated Data Infrastructure (IDI) from Statistics New Zealand. The visualisation suggests that over the last demi-decade, housing unaffordability has partially distorted the commuting patterns of key workers in Auckland. More of the working population, in particular those key workers, are displaced to the outer rings of the city. While there is an overall reduction in excess commuting across three groups of workers, key workers remain the working population with a disproportionate long excess commute. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
Integrating Network Centrality and Node-Place Model to Evaluate and Classify Station Areas in Shanghai
ISPRS Int. J. Geo-Inf. 2021, 10(6), 414; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060414 - 16 Jun 2021
Viewed by 442
Abstract
Transit-oriented development (TOD) is generally understood as an effective urban design model for encouraging the use of public transportation. Inspired by TOD, the node-place (NP) model was developed to investigate the relationship between transport stations and land use. However, existing studies construct the [...] Read more.
Transit-oriented development (TOD) is generally understood as an effective urban design model for encouraging the use of public transportation. Inspired by TOD, the node-place (NP) model was developed to investigate the relationship between transport stations and land use. However, existing studies construct the NP model based on the statistical attributes, while the importance of travel characteristics is ignored, which arguably cannot capture the complete picture of the stations. In this study, we aim to integrate the NP model and travel characteristics with systematic insights derived from network theory to classify stations. A node-place-network (NPN) model is developed by considering three aspects: land use, transportation, and travel network. Moreover, the carrying pressure is proposed to quantify the transport service pressure of the station. Taking Shanghai as a case study, our results show that the travel network affects the station classification and highlights the imbalance between the built environment and travel characteristics. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
Heterogeneity of Spatial Distribution and Factors Influencing Unattended Locker Points in Guangzhou, China: The Case of Hive Box
ISPRS Int. J. Geo-Inf. 2021, 10(6), 409; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060409 - 14 Jun 2021
Viewed by 580
Abstract
Hive Box is a company that operates a network of express unattended collection and delivery points (UCDPs) in China. Hive Box distribution enhances community-based end-to-end delivery services and low-carbon city logistics. It is argued that UCDPs compared with attended collection and delivery points [...] Read more.
Hive Box is a company that operates a network of express unattended collection and delivery points (UCDPs) in China. Hive Box distribution enhances community-based end-to-end delivery services and low-carbon city logistics. It is argued that UCDPs compared with attended collection and delivery points (ACDPs) should be considered for further investigation. Therefore, the present study employed kernel density estimation, spatial autocorrelation analysis, and geographically weighted regression to investigate the spatial heterogeneity of Hive Box distribution across Guangzhou. Hive Box location data were collected from smartphone apps. The results were as follows: (1) the kernel density declined from the city center toward the outskirts, and showed point-like spatial agglomerations in the city center; (2) the Moran’s I index analysis showed that Hive Box distribution exhibited spatial agglomeration from a global perspective and geographic variations in locality in space; the heterogeneity of urban–rural differences implies the uneven development of Hive Box distribution in Guangzhou; and (3) the factors influencing Hive Box distribution were multilevel, and their effects were complex and varied across regions. These results shed light on the agglomeration and heterogeneity characteristics of the spatial distribution and influencing factors of Hive Boxes. For an enhanced community-based end-to-end delivery service, this study suggested the identification of the geographic variations of Hive Box distribution and the combined effects of multiple factors in intensifying the infrastructure of unattended locker points. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
A New Data-Enabled Intelligence Framework for Evaluating Urban Space Perception
ISPRS Int. J. Geo-Inf. 2021, 10(6), 400; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10060400 - 09 Jun 2021
Cited by 1 | Viewed by 680
Abstract
The urban environment has a great impact on the wellbeing of citizens and it is of great significance to understand how citizens perceive and evaluate places in a large scale urban region and to provide scientific evidence to support human-centered urban planning with [...] Read more.
The urban environment has a great impact on the wellbeing of citizens and it is of great significance to understand how citizens perceive and evaluate places in a large scale urban region and to provide scientific evidence to support human-centered urban planning with a better urban environment. Existing studies for assessing urban perception have primarily relied on low efficiency methods, which also result in low evaluation accuracy. Furthermore, there lacks a sophisticated understanding on how to correlate the urban perception with the built environment and other socio-economic data, which limits their applications in supporting urban planning. In this study, a new data-enabled intelligence framework for evaluating human perceptions of urban space is proposed. Specifically, a novel classification-then-regression strategy based on a deep convolutional neural network and a random-forest algorithm is proposed. The proposed approach has been applied to evaluate the perceptions of Beijing and Chengdu against six perceptual criteria. Meanwhile, multi-source data were employed to investigate the associations between human perceptions and the indicators for the built environment and socio-economic data including visual elements, facility attributes and socio-economic indicators. Experimental results show that the proposed framework can effectively evaluate urban perceptions. The associations between urban perceptions and the visual elements, facility attributes and a socio-economic dimension have also been identified, which can provide substantial inputs to guide the urban planning for a better urban space. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
Public Bike Trip Purpose Inference Using Point-of-Interest Data
ISPRS Int. J. Geo-Inf. 2021, 10(5), 352; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050352 - 20 May 2021
Viewed by 608
Abstract
Public bike-sharing is eco-friendly, connects excellently with other transportation modes, and provides a means of mobility that is highly suitable in the current era of climate change. This study proposes a methodology for inferring the bike trip purpose based on bike-share and point-of-interest [...] Read more.
Public bike-sharing is eco-friendly, connects excellently with other transportation modes, and provides a means of mobility that is highly suitable in the current era of climate change. This study proposes a methodology for inferring the bike trip purpose based on bike-share and point-of-interest (POI) data. Because the purpose of a trip involves decision-making, its inference necessitates an understanding of the spatiotemporal complexity of human activities. Thus, the spatiotemporal features affecting bike trips were selected from the bike-share data, and the land uses at the origin and destination of the trips were extracted from the POI data. During POI type embedding, the data were augmented considering the geographical distance between the POIs and the number of bike rentals at each bike station. We further developed a ground truth data construction method that uses temporal mobile and POI data. The inference model was built using machine learning and applied to experiments involving bike stations in Seocho-gu, Seoul, Korea. The experimental results revealed that optimal performance was achieved with the use of decision tree algorithms, as demonstrated by a 78.95% overall accuracy and 66.43% F1-score. The proposed method contributes to a better understanding of the causes of movement within cities. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
A Proposed Framework for Identification of Indicators to Model High-Frequency Cities
ISPRS Int. J. Geo-Inf. 2021, 10(5), 317; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050317 - 08 May 2021
Viewed by 662
Abstract
A city is a complex system that never sleeps; it constantly changes, and its internal mobility (people, vehicles, goods, information, etc.) continues to accelerate and intensify. These changes and mobility vary in terms of the attributes of the city, such as space, time [...] Read more.
A city is a complex system that never sleeps; it constantly changes, and its internal mobility (people, vehicles, goods, information, etc.) continues to accelerate and intensify. These changes and mobility vary in terms of the attributes of the city, such as space, time and cultural affiliation, which characterise to some extent how the city functions. Traditional urban studies have successfully modelled the ‘low-frequency city’ and have provided solutions such as urban planning and highway design for long-term urban development. Nevertheless, the existing urban studies and theories are insufficient to model the dynamics of a city’s intense mobility and rapid changes, so they cannot tackle short-term urban problems such as traffic congestion, real-time transport scheduling and resource management. The advent of information and communication technology and big data presents opportunities to model cities with unprecedented resolution. Since 2018, a paradigm shift from modelling the ‘low-frequency city’ to the so-called ‘high-frequency city’ has been introduced, but hardly any research investigated methods to estimate a city’s frequency. This work aims to propose a framework for the identification and analysis of indicators to model and better understand the concept of a high-frequency city in a systematic manner. The methodology for this work was based on a content analysis-based review, taking into account specific criteria to ensure the selection of indicator sets that are consistent with the concept of the frequency of cities. Twenty-two indicators in five groups were selected as indicators for a high-frequency city, and a framework was proposed to assess frequency at both the intra-city and inter-city levels. This work would serve as a pilot study to further illuminate the ways that urban policy and operations can be adjusted to improve the quality of city life in the context of a smart city. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
Hotspot Detection and Spatiotemporal Evolution of Catering Service Grade in Mountainous Cities from the Perspective of Geo-Information Tupu
ISPRS Int. J. Geo-Inf. 2021, 10(5), 287; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10050287 - 30 Apr 2021
Viewed by 485
Abstract
Catering services are an essential part of urban life. The spatial structure and evolution of catering services can reflect the characteristics of an urban structure to a certain extent. In this study, we selected the main urban area of Chongqing, a typical mountainous [...] Read more.
Catering services are an essential part of urban life. The spatial structure and evolution of catering services can reflect the characteristics of an urban structure to a certain extent. In this study, we selected the main urban area of Chongqing, a typical mountainous city, as the research area. According to data sources for 200,000 catering POI data points in 2015 and 2020, we extracted the hotspots according to catering service grade based on kernel density. We quantitatively analyzed the spatiotemporal structure of catering services in the mountainous city. In addition, we used digital field hierarchical structure Tupu and generalized symmetric structure Tupu to identify the spatial morphology and evolution characteristics to enhance the understanding of geoscience trends. The results showed that (1) the distribution of catering services was statically consistent with the “multi-center group” distribution of the mountainous city and dynamically similar to the “sprawling leap” development of the mountainous city where it developed from independent points to cross mountains and rivers. Moreover, we found that there was a tendency of adhering development between groups. (2) From the perspective of symmetrical distribution, the symmetrical distribution of the catering industry reflected a certain generalized symmetrical structure with mountains and rivers in the mountainous city. Furthermore, the city tended to develop symmetrically along the topography, thus forming the symmetry of economic geography. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
What Happens in the City When Long-Term Urban Expansion and (Un)Sustainable Fringe Development Occur: The Case Study of Rome
ISPRS Int. J. Geo-Inf. 2021, 10(4), 231; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040231 - 06 Apr 2021
Viewed by 396
Abstract
This study investigates long-term landscape transformations (1949–2016) in urban Rome, Central Italy, through a spatial distribution of seven metrics (core, islet, perforation, edge, loop, bridge, branch) derived from a Morphological Spatial Pattern Analysis (MSPA) analyzed separately for seven land-use classes (built-up areas, arable [...] Read more.
This study investigates long-term landscape transformations (1949–2016) in urban Rome, Central Italy, through a spatial distribution of seven metrics (core, islet, perforation, edge, loop, bridge, branch) derived from a Morphological Spatial Pattern Analysis (MSPA) analyzed separately for seven land-use classes (built-up areas, arable land, crop mosaic, vineyards, olive groves, forests, pastures). A Principal Component Analysis (PCA) has been finally adopted to characterize landscape structure at 1949 and 2016. Results of the MSPA demonstrate how both natural and agricultural land-uses have decreased following urban expansion. Moreover, the percent ‘core’ area of each class declined substantially, although with different intensity. These results clearly indicate ‘winners’ and ‘losers’ after long-term landscape transformations: urban settlements and forests belong to the former category, the remaining land-use classes (mostly agricultural) belong to the latter category. Descriptive statistics and multivariate exploratory techniques finally documented the intrinsic complexity characteristic of actual landscapes. The findings of this study also demonstrate how settlements have expanded chaotically over the study area, reflecting a progressive ‘fractalization’ and inhomogeneity of fringe landscapes, with negative implications for metropolitan sustainability at large. These transformations were unable to leverage processes of settlement and economic re-agglomeration around sub-centers typical of polycentric development in the most advanced socioeconomic contexts. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
Parking Places to Moped-Style Scooter Sharing Services Using GIS Location-Allocation Models and GPS Data
ISPRS Int. J. Geo-Inf. 2021, 10(4), 230; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040230 - 06 Apr 2021
Cited by 2 | Viewed by 653
Abstract
Moped-style scooters are one of the most popular systems of micro-mobility. They are undoubtedly good for the city, as they promote forms of environmentally-friendly mobility, in which flexibility helps prevent traffic build-up in the urban centers where they operate. However, their increasing numbers [...] Read more.
Moped-style scooters are one of the most popular systems of micro-mobility. They are undoubtedly good for the city, as they promote forms of environmentally-friendly mobility, in which flexibility helps prevent traffic build-up in the urban centers where they operate. However, their increasing numbers are also generating conflicts as a result of the bad behavior of users, their unwarranted use in public spaces, and above all their parking. This paper proposes a methodology for finding parking spaces for shared motorcycle services using Geographic information system (GIS) location-allocation models and Global Positioning System (GPS) data. We used the center of Madrid and data from the company Muving (one of the city’s main operators) for our case study. As well as finding the location of parking spaces for motorbikes, our analysis examines how the varying distribution of demand over the course of the day affects the demand allocated to parking spaces. The results demonstrate how reserving a relatively small number of parking spaces for scooters makes it possible to capture over 70% of journeys in the catchment area. The daily variations in the distribution of demand slightly reduce the efficiency of the network of parking spaces in the morning and increase it at night, when demand is strongly focused on the most central areas. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
Modeling Past, Present, and Future Urban Growth Impacts on Primary Agricultural Land in Greater Irbid Municipality, Jordan Using SLEUTH (1972–2050)
ISPRS Int. J. Geo-Inf. 2021, 10(4), 212; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040212 - 01 Apr 2021
Cited by 1 | Viewed by 670
Abstract
Urban expansion and loss of primarily agricultural land are two of the challenges facing Jordan. Located in the most productive agricultural area of Jordan, Greater Irbid Municipality (GIM) uncontrolled urban growth has posed a grand challenge in both sustaining its prime croplands and [...] Read more.
Urban expansion and loss of primarily agricultural land are two of the challenges facing Jordan. Located in the most productive agricultural area of Jordan, Greater Irbid Municipality (GIM) uncontrolled urban growth has posed a grand challenge in both sustaining its prime croplands and developing comprehensive planning strategies. This study investigated the loss of agricultural land for urban growth in GIM from 1972–2050 and denoted the negative consequences of the amalgamation process of 2001 on farmland loss. The aim is to unfold and track historical land use/cover changes and forecast these changes to the future using a modified SLEUTH-3r urban growth model. The accuracy of prediction results was assessed in three different sites between 2015 and 2020. In 43 years the built-up area increased from 29.2 km2 in 1972 to 71 km2 in 2015. By 2050, the built-up urban area would increase to 107 km2. The overall rate of increase, however, showed a decline across the study period, with the periods of 1990–2000 and 2000–2015 having the highest rate of built-up areas expansion at 68.6 and 41.4%, respectively. While the agricultural area increased from 178 km2 in 1972 to 207 km2 in 2000, it decreased to 195 km2 in 2015 and would continue to decrease to 188 km2 by 2050. The district-level analysis shows that from 2000–2015, the majority of districts exhibited an urban increase at twice the rate of 1990–2000. The results of the net change analysis of agriculture show that between 1990 and 2000, 9 districts exhibited a positive gain in agricultural land while the rest of the districts showed a negative loss of agricultural land. From 2000 to 2015, the four districts of Naser, Nozha, Rawdah, and Hashmyah completely lost their agricultural areas for urbanization. By 2050, Idoon and Boshra districts will likely lose more than half of their high-quality agricultural land. This study seeks to utilize a spatially explicit urban growth model to support sustainable planning policies for urban land use through forecasting. The implications from this study confirm the worldwide urbanization impacts on losing the most productive agricultural land in the outskirts and consequences on food production and food security. The study calls for urgent actions to adopt a compact growth policy with no new land added for development as what is available now exceeds what is needed by 2050 to accommodate urban growth in GIM. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
CrimeVec—Exploring Spatial-Temporal Based Vector Representations of Urban Crime Types and Crime-Related Urban Regions
ISPRS Int. J. Geo-Inf. 2021, 10(4), 210; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040210 - 01 Apr 2021
Viewed by 607
Abstract
The traditional categorization of crime types relies on a hierarchical structure, from high-level categories to lower-level subtypes. This tree-based classification treats crime types as mutually independent when they do not branch from the same higher-level category, therefore lacking inter-category semantic relations. The issue [...] Read more.
The traditional categorization of crime types relies on a hierarchical structure, from high-level categories to lower-level subtypes. This tree-based classification treats crime types as mutually independent when they do not branch from the same higher-level category, therefore lacking inter-category semantic relations. The issue then extends over crime distribution analysis of urban regions, often reporting statistics based on crime type counts, but neglecting implicit relations between different crime categories. Our study aims to fill this information gap, providing a more complete understanding of urban crime in both qualitative and quantitative terms. Specifically, we propose a vector-based crime type representation, constructed via unsupervised machine learning on temporal and geographic factors. The general idea is to define crime types as “related” if they often occur in the same area at the same time span, regardless of any initial hierarchical categorization. This opens to a new metric of comparison that goes beyond pre-defined structures, revealing hidden relationships between crime types by generating a vector space in a completely data-driven manner. Crime types are represented as points in this space, and their relative distances disclose stronger or weaker semantic relations. A direct application on urban crime distribution analysis stands out in the form of visualization tools for intuitive data investigations and convenient comparison measures on composite vectors of urban regions. Meaningful insights on crime type distributions and a better understanding of urban crime characteristics determine a valuable asset to urban management and development. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
Utilizing Urban Geospatial Data to Understand Heritage Attractiveness in Amsterdam
ISPRS Int. J. Geo-Inf. 2021, 10(4), 198; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040198 - 25 Mar 2021
Cited by 1 | Viewed by 723
Abstract
Touristic cities are home to historical landmarks and irreplaceable urban heritages. Although tourism brings financial advantages, mass tourism creates pressure on historical cities. Therefore, “attractiveness” is one of the key elements to explain tourism dynamics. User-contributed and geospatial data provide an evidence-based understanding [...] Read more.
Touristic cities are home to historical landmarks and irreplaceable urban heritages. Although tourism brings financial advantages, mass tourism creates pressure on historical cities. Therefore, “attractiveness” is one of the key elements to explain tourism dynamics. User-contributed and geospatial data provide an evidence-based understanding of people’s responses to these places. In this article, the combination of multisource information about national monuments, supporting products (i.e., attractions, museums), and geospatial data are utilized to understand attractive heritage locations and the factors that make them attractive. We retrieved geotagged photographs from the Flickr API, then employed density-based spatial clustering of applications with noise (DBSCAN) algorithm to find clusters. Then combined the clusters with Amsterdam heritage data and processed the combined data with ordinary least square (OLS) and geographically weighted regression (GWR) to identify heritage attractiveness and relevance of supporting products in Amsterdam. The results show that understanding the attractiveness of heritages according to their types and supporting products in the surrounding built environment provides insights to increase unattractive heritages’ attractiveness. That may help diminish the burden of tourism in overly visited locations. The combination of less attractive heritage with strong influential supporting products could pave the way for more sustainable tourism in Amsterdam. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
Landscape Visual Sensitivity Assessment of Historic Districts—A Case Study of Wudadao Historic District in Tianjin, China
ISPRS Int. J. Geo-Inf. 2021, 10(3), 175; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10030175 - 17 Mar 2021
Viewed by 843
Abstract
Against the backdrop of urban stock renewal, as the core area of a city rich in culture, aesthetics, and tourism resources, the assessment of landscape visual sensitivity of historic districts can provide an accurate, objective, and intuitive decision-making basis for the multi-purpose planning [...] Read more.
Against the backdrop of urban stock renewal, as the core area of a city rich in culture, aesthetics, and tourism resources, the assessment of landscape visual sensitivity of historic districts can provide an accurate, objective, and intuitive decision-making basis for the multi-purpose planning of districts. The main purpose of this study was to develop an assessment method based on the geographic information system (GIS) in order to make a visual sensitivity index map on a district scale. To this end, this study uses the multi-criteria evaluation (MCE) method, selects the visibility (VSv), the number of potential users (VSu), and remarkableness (VSe) as the main criteria, and constructs a comprehensive assessment model of the visual sensitivity of the historic landscape. The most well-protected Wudadao Historic District in Tianjin (Wudadao) was selected as the study area, and its visual sensitivity was assessed. The assessment results are divided into four levels: areas of high sensitivity, moderate sensitivity, low sensitivity, and very low sensitivity. Results indicate that after the optimization and improvement of the evaluation index for visual sensitivity of a large-scale forest landscape, it is feasible to evaluate the small-scale visual sensitivity of historic districts; the higher the sensitivity level, the more important it is to be protected, and the more cautious it should be in the renewal of districts; the higher the number of potential users, the higher the visual sensitivity level, and so on. Further attention needs to be paid to planning and design to improve visual quality. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
Deep Learning-Based Generation of Building Stock Data from Remote Sensing for Urban Heat Demand Modeling
ISPRS Int. J. Geo-Inf. 2021, 10(1), 23; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010023 - 12 Jan 2021
Cited by 2 | Viewed by 1030
Abstract
Cities are responsible for a large share of the global energy consumption. A third of the total greenhouse gas emissions are related to the buildings sector, making it an important target for reducing urban energy consumption. Detailed data on the building stock, including [...] Read more.
Cities are responsible for a large share of the global energy consumption. A third of the total greenhouse gas emissions are related to the buildings sector, making it an important target for reducing urban energy consumption. Detailed data on the building stock, including the thermal characteristics of individual buildings, such as the construction type, construction period, and building geometries, can strongly support decision-making for local authorities to help them spatially localize buildings with high potential for thermal renovations. In this paper, we present a workflow for deep learning-based building stock modeling using aerial images at a city scale for heat demand modeling. The extracted buildings are used for bottom-up modeling of the residential building heat demand based on construction type and construction period. The results for DL-building extraction exhibit F1-accuracies of 87%, and construction types yield an overall accuracy of 96%. The modeled heat demands display a high level of agreement of R2 0.82 compared with reference data. Finally, we analyze various refurbishment scenarios for construction periods and construction types, e.g., revealing that the targeted thermal renovation of multi-family houses constructed between the 1950s and 1970s accounts for about 47% of the total heat demand in a realistic refurbishment scenario. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
A Refined Lines/Regions and Lines/Lines Topological Relations Model Based on Whole-Whole Objects Intersection Components
ISPRS Int. J. Geo-Inf. 2021, 10(1), 15; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10010015 - 06 Jan 2021
Viewed by 661
Abstract
Refined topological relations play an important role in spatial database quality control. Currently, there is no unified and reasonable method to represent refined line/region and line/line topological relations in two-dimensional (2D) space. In addition, the existing independent line/region and line/line models have some [...] Read more.
Refined topological relations play an important role in spatial database quality control. Currently, there is no unified and reasonable method to represent refined line/region and line/line topological relations in two-dimensional (2D) space. In addition, the existing independent line/region and line/line models have some drawbacks such as incomplete type discrimination and too many topological invariants. In this paper, a refined line/region and line/line topological relations are represented uniformly by the sequence, dimension, and topological type of the intersection components. To make the relevant definitions conform to the traditional cognitions in 2D Euclidean space, the (simple) spatial object is defined based on manifold topology, and the spatial intersection components are defined based on the whole-whole object intersection set. Then the topological invariant of node degree is introduced, and the adjacent point kinds (e.g., “Null”, “On”, “In”, and “Out”) are defined to distinguish the intersection component types. Excluding impossible and symmetrical types, 29 types of intersection-lines (including 21 between lines/regions and 8 between lines/lines), and 6 types of intersection-points (including 2 between lines/regions and 4 between lines/lines) are classified. On this basis, a node degree-based whole-whole object intersection sets (N-WWIS) model for refined line/region and line/line topological relations is presented, and it can be combined with the Euler number-based whole object intersection and difference (E-WID) model (coarse level) to form a hierarchical representation method of topological relations. Furthermore, a prototype system based on the N-WWIS model for automatic topological integrity checking is developed and some evaluation experiments are conducted with OpenStreetMap (OSM) data is presented based on the classification of intersection components. The experimental results show that the N-WWIS model will enable the geographic information systems (GIS) community to develop automated topological conflict checking and dealing tools for spatial data updates and quality control. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
Opportunities and Challenges of Geospatial Analysis for Promoting Urban Livability in the Era of Big Data and Machine Learning
ISPRS Int. J. Geo-Inf. 2020, 9(12), 752; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120752 - 15 Dec 2020
Cited by 1 | Viewed by 1502
Abstract
Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challenges of urban complex systems requires [...] Read more.
Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challenges of urban complex systems requires advanced interdisciplinary analysis methods, such as urban informatics or urban data science. Yet, by applying a purely data-driven approach, it is too easy to get lost in the ‘forest’ of data, and to miss the ‘trees’ of successful, livable cities that are the ultimate aim of urban planning. This paper assesses how geospatial data, and urban analysis, using a mixed methods approach, can help to better understand urban dynamics and human behavior, and how it can assist planning efforts to improve livability. Based on reviewing state-of-the-art research the paper goes one step further and also addresses the potential as well as limitations of new data sources in urban analytics to get a better overview of the whole ‘forest’ of these new data sources and analysis methods. The main discussion revolves around the reliability of using big data from social media platforms or sensors, and how information can be extracted from massive amounts of data through novel analysis methods, such as machine learning, for better-informed decision making aiming at urban livability improvement. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
A Study of User Activity Patterns and the Effect of Venue Types on City Dynamics Using Location-Based Social Network Data
ISPRS Int. J. Geo-Inf. 2020, 9(12), 733; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120733 - 07 Dec 2020
Cited by 2 | Viewed by 734
Abstract
The main purpose of this research is to study the effect of various types of venues on the density distribution of residents and model check-in data from a Location-Based Social Network for the city of Shanghai, China by using combination of multiple temporal, [...] Read more.
The main purpose of this research is to study the effect of various types of venues on the density distribution of residents and model check-in data from a Location-Based Social Network for the city of Shanghai, China by using combination of multiple temporal, spatial and visualization techniques by classifying users’ check-ins into different venue categories. This article investigates the use of Weibo for big data analysis and its efficiency in various categories instead of manually collected datasets, by exploring the relation between time, frequency, place and category of check-in based on location characteristics and their contributions. The data used in this research was acquired from a famous Chinese microblogs called Weibo, which was preprocessed to get the most significant and relevant attributes for the current study and transformed into Geographical Information Systems format, analyzed and, finally, presented with the help of graphs, tables and heat maps. The Kernel Density Estimation was used for spatial analysis. The venue categorization was based on nature of the physical locations within the city by comparing the name of venue extracted from Weibo dataset with the function such as education for schools or shopping for malls and so on. The results of usage patterns from hours to days, venue categories and frequency distribution into these categories as well as the density of check-in within the Shanghai and contribution of each venue category in its diversity are thoroughly demonstrated, uncovering interesting spatio-temporal patterns including frequency and density of users from different venues at different time intervals, and significance of using geo-data from Weibo to study human behavior in variety of studies like education, tourism and city dynamics based on location-based social networks. Our findings uncover various aspects of activity patterns in human behavior, the significance of venue classes and its effects in Shanghai, which can be applied in pattern analysis, recommendation systems and other interactive applications for these classes. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
Spatio-Temporal Relationship between Land Cover and Land Surface Temperature in Urban Areas: A Case Study in Geneva and Paris
ISPRS Int. J. Geo-Inf. 2020, 9(10), 593; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100593 - 10 Oct 2020
Cited by 1 | Viewed by 1061
Abstract
Currently, more than half of the world’s population lives in cities, which leads to major changes in land use and land surface temperature (LST). The associated urban heat island (UHI) effects have multiple impacts on energy consumption and human health. A better understanding [...] Read more.
Currently, more than half of the world’s population lives in cities, which leads to major changes in land use and land surface temperature (LST). The associated urban heat island (UHI) effects have multiple impacts on energy consumption and human health. A better understanding of how different land covers affect LST is necessary for mitigating adverse impacts, and supporting urban planning and public health management. This study explores a distance-based, a grid-based and a point-based analysis to investigate the influence of impervious surfaces, green area and waterbodies on LST, from large (distance and grid based analysis with 400 m grids) to smaller (point based analysis with 30 m grids) scale in the two mid-latitude cities of Paris and Geneva. The results at large scale confirm that the highest LST was observed in the city centers. A significantly positive correlation was observed between LST and impervious surface density. An anticorrelation between LST and green area density was observed in Paris. The spatial lag model was used to explore the spatial correlation among LST, NDBI, NDVI and MNDWI on a smaller scale. Inverse correlations between LST and NDVI and MNDWI, respectively, were observed. We conclude that waterbodies display the greatest mitigation on LST and UHI effects both on the large and smaller scale. Green areas play an important role in cooling effects on the smaller scale. An increase of evenly distributed green area and waterbodies in urban areas is suggested to lower LST and mitigate UHI effects. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
Urban Green Accessibility Index: A Measure of Pedestrian-Centered Accessibility to Every Green Point in an Urban Area
ISPRS Int. J. Geo-Inf. 2020, 9(10), 586; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9100586 - 06 Oct 2020
Cited by 2 | Viewed by 985
Abstract
Advancements in remote sensing techniques and urban data analysis tools have enabled the successful monitoring and detection of green spaces in a city. This study aims to develop an index called the urban green accessibility (UGA) index, which measures people’s accessibility to green [...] Read more.
Advancements in remote sensing techniques and urban data analysis tools have enabled the successful monitoring and detection of green spaces in a city. This study aims to develop an index called the urban green accessibility (UGA) index, which measures people’s accessibility to green space and represents the citywide or local characteristics of the distribution pattern of green space. The index is defined as the sum of pedestrians’ accessibility to all vegetation points, which consists of the normalized difference vegetation index (NDVI) with integration and choice values from angular segment analysis. In this study, the proposed index is tested with cases of New York, NY, and San Francisco, CA, in the US. The results reveal differences based on the significance of streets. When analysis ranges are on a neighborhood scale, a few hotspots appear in well-known green areas on commonly accessible streets and in local neighborhood parks on residential blocks. The appearance of high-accessibility points in low-NDVI areas implies the potential of the efficient and proper distribution of green spaces for pedestrians. The proposed measure is expected to help in planning and managing green areas in cities, taking people’s accessibility and spatial relationships into consideration. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
Spatial Analysis of Settlement Structures to Identify Pattern Formation Mechanisms in Inter-Urban Systems
ISPRS Int. J. Geo-Inf. 2020, 9(9), 541; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090541 - 08 Sep 2020
Cited by 1 | Viewed by 1051
Abstract
Dissipative structures known from non-equilibrium thermodynamics can form patterns. Cities are regarded as open, dissipative structures due to their self-organisation and thus in theory are also capable of pattern formation. In a first step to understand similarities between nonlinear pattern formation and inter-urban [...] Read more.
Dissipative structures known from non-equilibrium thermodynamics can form patterns. Cities are regarded as open, dissipative structures due to their self-organisation and thus in theory are also capable of pattern formation. In a first step to understand similarities between nonlinear pattern formation and inter-urban systems, we investigate how inter-urban structures are arranged. We use data from the Global Urban Footprint to identify spatial regularities in seven regions (Argentina, China, Egypt, France, India, Ghana and USA) and to quantitatively describe settlement patterns by number of objects and density. We find that small areas of the examined data sets show a regular arrangement, the density and number of settlements differ widely between the different regions and the portion of regular areas within this regions strongly correlates with these two parameters. The results can be used to develop mathematical models that describe inter-urban pattern formation on the one hand and to investigate to what extent the respective settlement patterns are related to infrastructural, economic or political boundary conditions on the other. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
Analyzing the Influence of Urban Street Greening and Street Buildings on Summertime Air Pollution Based on Street View Image Data
ISPRS Int. J. Geo-Inf. 2020, 9(9), 500; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9090500 - 21 Aug 2020
Cited by 6 | Viewed by 1042
Abstract
Transport emissions and street dust are important sources of summertime air pollution in urban centers. Street greening and buildings have an influence on the diffusion of air pollution from streets. For field measurements, many studies have analyzed the effect of street green space [...] Read more.
Transport emissions and street dust are important sources of summertime air pollution in urban centers. Street greening and buildings have an influence on the diffusion of air pollution from streets. For field measurements, many studies have analyzed the effect of street green space arrangement on the diffusion of air pollution, but these studies have neglected the patterns at the landscape scale. Other studies have analyzed the effects of the large scale of green space on air pollution, but the vertical distribution of street buildings and greening has rarely been considered. In this study, we analyzed the impact of the vertical distribution of urban street green space on summertime air pollution in urban centers on the urban scale for the first time by using a deep-learning method to extract the vertical distribution of street greening and buildings from street view image data. A total of 687,354 street view images were collected. The green index and building index were proposed to quantify the street greening and street buildings. The multilevel regression method was used to analyze the association between the street green index, building index and air pollution indexes. For the cases in this study, including the central urban areas of Beijing, Shanghai and Nanjing, our multilevel regressions results suggested that, in the central area of the city, the vertical distribution of street greening and buildings within a certain range of the monitoring site is association with the summertime air pollution index of the monitoring site. There was a significant negative association between the street greening and air pollution indexes (radius = 1–2 km, NO2, p = 0.042; radius = 3–4 km, AQI, p = 0.034; PM10, p = 0.028). The street length within a certain range of the monitoring site has a positive association with the air pollution indexes (radius = 1–2 km, AQI, p = 0.072; PM10, p = 0.062). With the increase of the distance between streets and the monitoring sites, the association between streets and air pollution indexes decreases. Our findings on the association between the vertical structure of street greening, street buildings and summertime air pollution in urban centers can support urban street planning. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
Analyzing Links between Spatio-Temporal Metrics of Built-Up Areas and Socio-Economic Indicators on a Semi-Global Scale
ISPRS Int. J. Geo-Inf. 2020, 9(7), 436; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9070436 - 11 Jul 2020
Cited by 3 | Viewed by 1613
Abstract
Manifold socio-economic processes shape the built and natural elements in urban areas. They thus influence both the living environment of urban dwellers and sustainability in many dimensions. Monitoring the development of the urban fabric and its relationships with socio-economic and environmental processes will [...] Read more.
Manifold socio-economic processes shape the built and natural elements in urban areas. They thus influence both the living environment of urban dwellers and sustainability in many dimensions. Monitoring the development of the urban fabric and its relationships with socio-economic and environmental processes will help to elucidate their linkages and, thus, aid in the development of new strategies for more sustainable development. In this study, we identified empirical and significant relationships between income, inequality, GDP, air pollution and employment indicators and their change over time with the spatial organization of the built and natural elements in functional urban areas. We were able to demonstrate this in 32 countries using spatio-temporal metrics, using geoinformation from databases available worldwide. We employed random forest regression, and we were able to explain 32% to 68% of the variability of socio-economic variables. This confirms that spatial patterns and their change are linked to socio-economic indicators. We also identified the spatio-temporal metrics that were more relevant in the models: we found that urban compactness, concentration degree, the dispersion index, the densification of built-up growth, accessibility and land-use/land-cover density and change could be used as proxies for some socio-economic indicators. This study is a first and fundamental step for the identification of such relationships at a global scale. The proposed methodology is highly versatile, the inclusion of new datasets is straightforward, and the increasing availability of multi-temporal geospatial and socio-economic databases is expected to empirically boost the study of these relationships from a multi-temporal perspective in the near future. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
Multidimensional Visualization and Processing of Big Open Urban Geospatial Data on the Web
ISPRS Int. J. Geo-Inf. 2020, 9(7), 434; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9070434 - 11 Jul 2020
Cited by 4 | Viewed by 2035
Abstract
The focus of this research is addressing a subset of the geovisualization (i.e., geographic visualization) challenges identified in the literature, namely multidimensional vector and raster geospatial data visualization. Moreover, the work implements an approach for multidimensional raster geospatial data processing. The results of [...] Read more.
The focus of this research is addressing a subset of the geovisualization (i.e., geographic visualization) challenges identified in the literature, namely multidimensional vector and raster geospatial data visualization. Moreover, the work implements an approach for multidimensional raster geospatial data processing. The results of this research are provided through a geoportal comprised of multiple applications that are related to 3D visualization of cities, ground deformation, land use and land cover and mobility. In a subset of the applications, the datasets handled are considered to be large in volume. The geospatial data were visualized on dynamic and interactive virtual globes to enable visual exploration. The geoportal is available on the web to enable cross-platform access to it. Furthermore, the geoportal was developed employing open standards, free and open source software (FOSS) and open data, most importantly to ensure interoperability and reduce the barriers to access it. The geoportal brings together various datasets, different both in terms of context and format employing numerous technologies. As a result, the existing web technologies for geovisualization and geospatial data processing were examined and exemplary and innovative software was developed to extend the state of the art. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
Fine-Scale Dasymetric Population Mapping with Mobile Phone and Building Use Data Based on Grid Voronoi Method
ISPRS Int. J. Geo-Inf. 2020, 9(6), 344; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9060344 - 26 May 2020
Cited by 1 | Viewed by 826
Abstract
Fine-scale population mapping is of great significance for capturing the spatial and temporal distribution of the urban population. Compared with traditional census data, population data obtained from mobile phone data has high availability and high real-time performance. However, the spatial distribution of base [...] Read more.
Fine-scale population mapping is of great significance for capturing the spatial and temporal distribution of the urban population. Compared with traditional census data, population data obtained from mobile phone data has high availability and high real-time performance. However, the spatial distribution of base stations is uneven, and the service boundaries remain uncertain, which brings significant challenges to the accuracy of dasymetric population mapping. This paper proposes a Grid Voronoi method to provide reliable spatial boundaries for base stations and to build a subsequent regression based on mobile phone and building use data. The results show that the Grid Voronoi method gives high fitness in building use regression, and further comparison between the traditional ordinary least squares (OLS) regression model and geographically weighted regression (GWR) model indicates that the building use data can well reflect the heterogeneity of urban geographic space. This method provides a relatively convenient and reliable idea for capturing high-precision population distribution, based on mobile phone and building use data. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
Development of the Theory of Six Value Aggregation Paths in Network Modeling for Spatial Analyses
ISPRS Int. J. Geo-Inf. 2020, 9(4), 234; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9040234 - 10 Apr 2020
Cited by 2 | Viewed by 731
Abstract
The dynamic development of spatial structures entails looking for new methods of spatial analysis. The aim of this article is to develop a new theory of space modeling of network structures according to six value aggregation paths: minimum and maximum value difference, minimum [...] Read more.
The dynamic development of spatial structures entails looking for new methods of spatial analysis. The aim of this article is to develop a new theory of space modeling of network structures according to six value aggregation paths: minimum and maximum value difference, minimum and maximum value decrease, and minimum and maximum value increase. The authors show how values presenting (describing) various phenomena or states in urban space can be designed as network structures. The dynamic development of spatial structures entails looking for new methods of spatial analysis. This study analyzes these networks in terms of their nature: random or scale-free. The results show that the paths of minimum and maximum value differences reveal one stage of the aggregation of those values. They generate many small network structures with a random nature. Next four value aggregation paths lead to the emergence of several levels of value aggregation and to the creation of scale-free hierarchical network structures. The models developed according to described theory present the quality of urban areas in various versions. The theory of six paths of value combination includes new measuring tools and methods which can impact quality of life and minimize costs of bad designs or space destructions. They are the proper tools for the sustainable development of urban areas. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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Article
Optimal Location Analysis of Delivery Parcel-Pickup Points Using AHP and Network Huff Model: A Case Study of Shiweitang Sub-District in Guangzhou City, China
ISPRS Int. J. Geo-Inf. 2020, 9(4), 193; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9040193 - 25 Mar 2020
Cited by 4 | Viewed by 1237
Abstract
The use of parcel-pickup points (PPPs) is an effective approach for solving the last-mile problem. However, few studies provide specific guidance for the optimal organization of PPPs. Here, a geographic information system(GIS)-based hybrid model was developed combining the widely used analytic hierarchy process [...] Read more.
The use of parcel-pickup points (PPPs) is an effective approach for solving the last-mile problem. However, few studies provide specific guidance for the optimal organization of PPPs. Here, a geographic information system(GIS)-based hybrid model was developed combining the widely used analytic hierarchy process (AHP) multi-criteria analysis method with the Huff model that predicts the number of visiting customers to determine the optimal facility for collaboration and service as a PPP. Using this model, a decision-maker can select the highest-ranking facility or use the fluctuation ranking graph to determine a priority list of candidate facilities according to the appropriate PPP service distance. Our findings suggest that the optimal candidate facility should be located near high population density areas, a dense road network, and few geographic barriers. The facility should have a high attractiveness value, long business hours, and convenient access to public transportation, cover a large, high-population area, and should be a retail chain store. Based on these findings, the AHP method can improve the accuracy of obtaining the facility attractiveness value using the Huff model. Facility attractiveness has a strong effect on the resulting number of customers in the case of acceptably long distances to residential buildings. Full article
(This article belongs to the Special Issue Geodata Science and Spatial Analysis in Urban Studies)
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