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Geographical Analysis, Urban Modelling, Spatial Statistics, Econometric and Multidimensional Evaluation...
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Geographical Analysis, Urban Modelling, Spatial Statistics, Econometric and Multidimensional Evaluation in Urban Environment

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

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 65060

Special Issue Editors

Dr. Giuseppe Borruso

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Guest Editor
Department of Economic, Business, Mathematics and Statistics (DEAMS), University of Trieste, Via Tigor, 22, 34124 Trieste, Italy
Interests: urban geography; smart cities; GIS; transport geography; urban planning; regional planning; networks; migrations; mobility; spatial analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Beniamino Murgante

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Guest Editor
School of Engineering, University of Basilicata, Viale dell’Ateneo Lucano, 10, 85100 Potenza, Italy
Interests: spatial planning; spatial simulation; geodemographics; geographic data analysis of socioeconomic and population data; planning 2.0; participation 2.0; e-democracy; e-participation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Carmelo Maria Torre

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Guest Editor
Department of Civil Engineering Sciences and Architecture (DiCAR), Polytechnic University of Bari, Via Orabona 4, 70125 Bari, Italy
Interests: real estate and urban economics; urban management; decision support systems in spatial planning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hartmut Asche

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Guest Editor
Hasso Plattner Institut, Digital Engineering, University of Potsdam, Potsdam, Germany
Interests: GIS; cartography; cartographic visualization; spatial analysis
Prof. Dr. Ginevra Balletto

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Guest Editor
Department of Civil and Environmental Engineering and Architecture (DICAAR), University of Cagliari, via Marengo 3, 09123 Cagliari, Italy
Interests: urban and regional planning; cultural heritage; urban governance and urban policies; urban governance and urban policies (hard and soft); sport in the city
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, the main problem in geographical analysis has been the lack of spatial data availability. Today, the wide diffusion of electronic devices containing geo-referenced information generates a great production of spatial data. Volunteered geographic information activities (e.g., OpenStreetMap, Wikimapia), strictly public initiatives (e.g., open data, spatial data infrastructures, geo-portals) and market-oriented projects (e.g., Google Earth, Bing Maps) have produced an overabundance of spatial data, which, in many cases, do not help toward the efficiency of decision processes.

Additionally, several urban policies stress the role of evaluation as support of sustainable urban development and preservation transformation plans and programs moving into a novel conceptual framework that integrates economic, ecological, and social dimension in spatial planning.

The increase of geographical data availability has not been fully coupled with an increase of knowledge to support spatial decisions.

In this perspective, the scientific effort is related to the identification of appropriate procedures to address the deliberative structure. In fact, through organizing and facilitating communication, it is possible to build a consensus among various decision-makers and interest groups and generate compromise and solutions that best represent the preferences of all those involved in the spatial decision-making process.

The inclusion of spatial simulation techniques in recent GIS software has favored the diffusion of these methods but in several cases led to a mechanism based on which buttons one has to press without having geography or processes in mind.

Spatial modeling, analytical techniques, and geographical analyses are therefore required in order to analyze data and to facilitate the decision process at all levels, with a clear identification of the geographical information needed and reference scale to adopt.

Old geographical issues can find an answer thanks to new methods and instruments, while new issues are developing, challenging researchers for new solutions.

This Special Issue aims at providing innovative and original contributions to the ongoing debate on the abovementioned issues. Further, it is aimed at illuminating the hot topics and the fresh experimentations and ideas in multicriteria, collaborative, adaptive, and synergistic decision-making, multidimensional appraisal, and econometrics, supported by spatial data infrastructures and ICT. These approaches are spread in many scientific fields, from traditional spatial urban economics, to real estate economics and to the newest bio-econometrics. The main topics of the Special Issue are related—but not limited—to multidimensional approaches in urban economics, from the econometric approach to qualitative evaluation, new analytical and empirical approaches, hard and soft fuzzy multicriteria analysis, multidimensional computing, bio-econometrics, spatial econometrics, and MCDM in environmental, cultural, and urban economics.

Prof. Dr. Giuseppe Borruso
Prof. Dr. Beniamino Murgante
Prof. Dr. Carmelo Maria Torre  
Prof. Dr. Hartmut Asche
Prof. Dr. Ginevra Balletto
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 submissions that pass pre-check are 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 1700 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

  • geographical analysis
  • urban modeling
  • spatial statistics
  • GIS
  • remote sensing
  • spatial planning
  • urban planning
  • geostatistics
  • multicriteria decision making
  • real estate values modeling
  • spatial econometrics and statistics
  • geostatistics
  • urban economics, urban geography

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Published Papers (14 papers)

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Research

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24 pages, 6319 KiB  
Article
Spatial Determinants of Real Estate Appraisals in The Netherlands: A Machine Learning Approach
by Evert Guliker, Erwin Folmer and Marten van Sinderen
ISPRS Int. J. Geo-Inf. 2022, 11(2), 125; https://doi.org/10.3390/ijgi11020125 - 9 Feb 2022
Cited by 17 | Viewed by 5017
Abstract
With the rapidly increasing house prices in the Netherlands, there is a growing need for more localised value predictions for mortgage collaterals within the financial sector. Many existing studies focus on modelling house prices for an individual city; however, these models are often [...] Read more.
With the rapidly increasing house prices in the Netherlands, there is a growing need for more localised value predictions for mortgage collaterals within the financial sector. Many existing studies focus on modelling house prices for an individual city; however, these models are often not interesting for mortgage lenders with assets spread out all over the country. That is why, with the current abundance of national geospatial datasets, this paper implements and compares three hedonic pricing models (linear regression, geographically weighted regression, and extreme gradient boosting—XGBoost) to model real estate appraisals values for five large municipalities in different parts of the Netherlands. The appraisal values used to train the model are provided by Stater N.V., which is the largest mortgage service provider in the Netherlands. Out of the three implemented models, the XGBoost model has the highest accuracy. XGBoost can explain 83% of the variance with an RMSE of €65,312, an MAE of €43,625, and an MAPE of 6.35% across the five municipalities. The two most important variables in the model are the total living area and taxation value, which were taken from publicly available datasets. Furthermore, a comparison is made between indexation and XGBoost, which shows that the XGBoost model is able to more accurately predict the appraisal values of different types of houses. The remaining unexplained variance is most probably caused by the lack of good indicators for the condition of the house. Overall, this paper highlights the benefits of open geospatial datasets to build a national real estate appraisal model. Full article
(This article belongs to the Special Issue Geographical Analysis, Urban Modelling, Spatial Statistics, Econometric and Multidimensional Evaluation in Urban Environment)
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13 pages, 2826 KiB  
Article
Spatiotemporal Distribution Patterns and Local Driving Factors of Regional Development in Java
by Andrea Emma Pravitasari, Ernan Rustiadi, Rista Ardy Priatama, Alfin Murtadho, Adib Ahmad Kurnia, Setyardi Pratika Mulya, Izuru Saizen, Candraningratri Ekaputri Widodo and Siti Wulandari
ISPRS Int. J. Geo-Inf. 2021, 10(12), 812; https://doi.org/10.3390/ijgi10120812 - 30 Nov 2021
Cited by 12 | Viewed by 4239
Abstract
Although uneven regional development has long been an issue in Java, most parts of the territory experienced an increased level of development over the last two decades. Due to the variance in local background and spatial heterogeneity, the driving factors of the development [...] Read more.
Although uneven regional development has long been an issue in Java, most parts of the territory experienced an increased level of development over the last two decades. Due to the variance in local background and spatial heterogeneity, the driving factors of the development level should, theoretically, vary over space. Therefore, in this study, we aim to investigate the local factors that influence the development level of Java’s regions. We used the spatiotemporal pattern analysis, ordinary least squares (OLS) regression, and geographically weighted regression (GWR), utilizing the regional development index as the predicted variable, and the social level, economy, infrastructure, land use, and environmental barriers as predictors. As per our results, it was found that the level of development in Java has improved over the past two decades. Metropolitan areas continued to lead this improvement. All the predictors that we examined significantly affected regional development. However, the spatial pattern of the local regression coefficients of Human Development Index (HDI), landslide, paddy conversion, and crime shifted due to changes in the spatial concentration of development activities. Full article
(This article belongs to the Special Issue Geographical Analysis, Urban Modelling, Spatial Statistics, Econometric and Multidimensional Evaluation in Urban Environment)
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19 pages, 2214 KiB  
Article
SDG Indicator 11.3.1 and Secondary Cities: An Analysis and Assessment
by Melinda Laituri, Danielle Davis, Faith Sternlieb and Kathleen Galvin
ISPRS Int. J. Geo-Inf. 2021, 10(11), 713; https://doi.org/10.3390/ijgi10110713 - 20 Oct 2021
Cited by 17 | Viewed by 4447
Abstract
Secondary cities are rapidly growing areas in low- and middle-income countries that lack data, planning, and essential services for sustainable development. Their rapid, informal growth patterns mean secondary cities are often data-poor and under-resourced, impacting the ability of governments to target development efforts, [...] Read more.
Secondary cities are rapidly growing areas in low- and middle-income countries that lack data, planning, and essential services for sustainable development. Their rapid, informal growth patterns mean secondary cities are often data-poor and under-resourced, impacting the ability of governments to target development efforts, respond to emergencies, and design sustainable futures. The United Nations’ Sustainable Development Goal (SDG) 11 focuses on inclusive, safe, resilient, and sustainable cities and human settlements. SDG Indicator (SDGI) 11.3.1 calculates the ratio of land consumption rate to population growth rate to enhance inclusive and sustainable urbanization. Our paper compares three cities—Denpasar, Indonesia; Kharkiv, Ukraine; and Mekelle, Ethiopia—that were part of the Secondary Cities (2C) Initiative of the U.S. Department of State, Office of the Geographer and Global Issues to assess SDGI 11.3.1. The 2C Initiative focused on field-based participatory mapping for data generation to assist city planning. Urban form and population data are critical for calculating and visually representing this ratio. We examine the spatial extent of each city to assess land use efficiency (LUE) and track changes in urban form over time. With limited demographic and spatial data for secondary cities, we speculate whether SDGI 11.3.1 is useful for small- and medium-sized cities. Full article
(This article belongs to the Special Issue Geographical Analysis, Urban Modelling, Spatial Statistics, Econometric and Multidimensional Evaluation in Urban Environment)
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19 pages, 5178 KiB  
Article
Accessibility of Vaccination Centers in COVID-19 Outbreak Control: A GIS-Based Multi-Criteria Decision Making Approach
by Kadir Diler Alemdar, Ömer Kaya, Muhammed Yasin Çodur, Tiziana Campisi and Giovanni Tesoriere
ISPRS Int. J. Geo-Inf. 2021, 10(10), 708; https://doi.org/10.3390/ijgi10100708 - 16 Oct 2021
Cited by 19 | Viewed by 3848
Abstract
The most important protective measure in the pandemic process is a vaccine. The logistics and administration of the vaccine are as important as its production. The increasing diffusion of electronic devices containing geo-referenced information generates a large production of spatial data that are [...] Read more.
The most important protective measure in the pandemic process is a vaccine. The logistics and administration of the vaccine are as important as its production. The increasing diffusion of electronic devices containing geo-referenced information generates a large production of spatial data that are essential for risk management and impact mitigation, especially in the case of disasters and pandemics. Given that vaccines will be administered to the majority of people, it is inevitable to establish vaccination centres outside hospitals. Site selection of vaccination centres is a major challenge for the health sector in metropolitan cities due to the dense population and high number of daily cases. A poor site selection process can cause many problems for the health sector, workforce, health workers, and patients. To overcome this, a three-step solution approach is proposed: (i) determining eight criteria using from the experience of the advisory committee, (ii) calculating criterion weights using Analytic Hierarchy Process (AHP), and performing spatial analysis of criteria using Geographic Information System (GIS), (iii) assigning potential vaccination centres by obtaining a suitability map and determining service areas. A case study is performed for Bağcılar, Istanbul district, using the proposed methodology. The results show that the suitable areas are grouped in three different areas of the district. The proposed methodology provides an opportunity to execute a scientific and strategic vaccination programme and to create a map of suitable vaccination centres for the countries. Full article
(This article belongs to the Special Issue Geographical Analysis, Urban Modelling, Spatial Statistics, Econometric and Multidimensional Evaluation in Urban Environment)
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18 pages, 47130 KiB  
Article
Spatial Distribution and Mechanism of Urban Occupation Mixture in Guangzhou: An Optimized GeoDetector-Based Index to Compare Individual and Interactive Effects
by Xingdong Deng, Yang Liu, Feng Gao, Shunyi Liao, Fan Zhou and Guanfang Cai
ISPRS Int. J. Geo-Inf. 2021, 10(10), 659; https://doi.org/10.3390/ijgi10100659 - 30 Sep 2021
Cited by 20 | Viewed by 2965
Abstract
Numerous studies have been devoted to uncovering the characteristics of resident density and urban mobility with multisource geospatial big data. However, little attention has been paid to the internal diversity of residents such as their occupations, which is a crucial aspect of urban [...] Read more.
Numerous studies have been devoted to uncovering the characteristics of resident density and urban mobility with multisource geospatial big data. However, little attention has been paid to the internal diversity of residents such as their occupations, which is a crucial aspect of urban vibrancy. This study aims to investigate the variation between individual and interactive influences of built environment factors on occupation mixture index (OMI) with a novel GeoDetector-based indicator. This study first integrated application (App) use and mobility patterns from cellphone data to portray residents’ occupations and evaluate the OMI in Guangzhou. Then, the mechanism of OMI distribution was analyzed with the GeoDetector model. Next, an optimized GeoDetector-based index, interactive effect variation ratio (IEVR) was proposed to quantify the variation between individual and interactive effects of factors. The results showed that land use mixture was the dominating factor, and that land use mixture, building density, floor area ratio, road density affected the OMI distribution directly. Some interesting findings were uncovered by IEVR. The influences of cultural inclusiveness and metro accessibility were less important in factor detector result, while they were found to be the most influential in an indirect way interacting with other built environment factors. The results suggested that both “hardware facilities” (land use mixture, accessibility) and “soft facilities” (cultural inclusiveness) should be considered in planning a harmonious urban employment space and sustainable city. Full article
(This article belongs to the Special Issue Geographical Analysis, Urban Modelling, Spatial Statistics, Econometric and Multidimensional Evaluation in Urban Environment)
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19 pages, 7566 KiB  
Article
Spatiotemporal Analysis of Land Cover and the Effects on Ecosystem Service Values in Rupandehi, Nepal from 2005 to 2020
by Aman KC, Nimisha Wagle and Tri Dev Acharya
ISPRS Int. J. Geo-Inf. 2021, 10(10), 635; https://doi.org/10.3390/ijgi10100635 - 23 Sep 2021
Cited by 8 | Viewed by 6345
Abstract
Land cover (LC) is a crucial parameter for studying environmental phenomena. Cutting-edge technology such as remote sensing (RS) and cloud computing have made LC change mapping efficient. In this study, the LC of Rupandehi District of Nepal were mapped using Landsat imagery and [...] Read more.
Land cover (LC) is a crucial parameter for studying environmental phenomena. Cutting-edge technology such as remote sensing (RS) and cloud computing have made LC change mapping efficient. In this study, the LC of Rupandehi District of Nepal were mapped using Landsat imagery and Random Forest (RF) classifier from 2005 to 2020 using Google Earth Engine (GEE) platform. GEE eases the way in extracting, analyzing, and performing different operations for the earth’s observed data. Land cover classification, Centre of gravity (CoG), and their trajectories for all LC classes: agriculture, built-up, water, forest, and barren area were extracted with five-year intervals, along with their Ecosystem service values (ESV) to understand the load on the ecosystem. We also discussed the aspects and problems of the spatiotemporal analysis of developing regions. It was observed that the built-up areas had been increasing over the years and more centered in between the two major cities. Other agriculture, water, and forest classes had been subjected to fluctuations with barren land in the decreasing trend. This alteration in the area of the LC classes also resulted in varying ESVs for individual land cover and total values for the years. The accuracy for the RF classifier was under substantial agreement for such fragmented LCs. Using LC, CoG, and ESV, the paper discusses the need for spatiotemporal analysis studies in Nepal to overcome the current limitations and later expansion to other regions. Studies such as these help in implementing proper plans and strategies by district administration offices and local governmental bodies to stop the exploitation of resources. Full article
(This article belongs to the Special Issue Geographical Analysis, Urban Modelling, Spatial Statistics, Econometric and Multidimensional Evaluation in Urban Environment)
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19 pages, 3487 KiB  
Article
Distribution Patterns and Multilevel Factors of the Innovation Activities of China’s New Energy Vehicle Industry
by Kaihuang Zhang, Qinglan Qian and Zhixin Feng
ISPRS Int. J. Geo-Inf. 2021, 10(6), 385; https://doi.org/10.3390/ijgi10060385 - 4 Jun 2021
Cited by 5 | Viewed by 3238
Abstract
To better understand the multilevel mechanism of innovation and reveal the distribution and geographical formation of China’s NEV industry innovation, we collect patent data in China’s NEV industry from SIPO and use ArcGIS and HLM to map and analyze its geoinformation from 2009 [...] Read more.
To better understand the multilevel mechanism of innovation and reveal the distribution and geographical formation of China’s NEV industry innovation, we collect patent data in China’s NEV industry from SIPO and use ArcGIS and HLM to map and analyze its geoinformation from 2009 to 2014. The results show that innovation activities are agglomerated at different scales, but the distribution of individual innovation and collaborative innovation are not similar, especially Pearl River Delta, which shows a great shortage of collaborative innovation. The multilevel effects are complicated. Institutional environment, resources, the regional institutional environment, and infrastructure have positive effects at the local level. Institutional environment is the only positive factor at the regional level. In general, regional factors enhance the positive effects of local factors. However, multilevel mechanism of collaborative innovation is different from the individual innovation, as it is less depended on policies stimulation and local knowledge institutions play a more important role in it. Furthermore, we put forward multilevel policies suggestions that central government should pay more attention on NEV products regulations, institutional environment should be improved at regional and local government should develop knowledge facilities in post-subsidy era. Our study highlights that locals that have advantages would benefit more from multilevel systems. Full article
(This article belongs to the Special Issue Geographical Analysis, Urban Modelling, Spatial Statistics, Econometric and Multidimensional Evaluation in Urban Environment)
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29 pages, 10620 KiB  
Article
Geospatial Analysis of Solar Energy in Riyadh Using a GIS-AHP-Based Technique
by Lamya Albraheem and Leena Alabdulkarim
ISPRS Int. J. Geo-Inf. 2021, 10(5), 291; https://doi.org/10.3390/ijgi10050291 - 3 May 2021
Cited by 29 | Viewed by 5894
Abstract
In recent years, spatial multi-criteria decision analysis (MCDA) has been applied to different types of spatial problems, such as solar power site suitability. MCDA can be used to support the process of identifying suitable regions for solar energy projects. To the best of [...] Read more.
In recent years, spatial multi-criteria decision analysis (MCDA) has been applied to different types of spatial problems, such as solar power site suitability. MCDA can be used to support the process of identifying suitable regions for solar energy projects. To the best of our knowledge, no study has addressed the problem of site evaluation for solar photovoltaic PV systems in the Riyadh region. Therefore, a spatial MCDA framework is proposed to perform a geospatial analysis of solar energy in the Riyadh region, which includes data collection, spatial analysis, a spatial decision support system and visualization. The methodology that was used to solve the site suitability problem is described. It involved the combination of a Geographical Information System (GIS) and the Analytic Hierarchy Process (GIS-AHP). The results show that the most suitable sites are in the north and northwest of the Riyadh region, with an area that represents 16,748 Km2 with an 80% suitability degree. In addition, it was proven that Afif is the largest suitable city. It has high solar radiation, at an average of 2.631687 MWh/m2/year, and low temperatures, at an average of 26.3 °C, as well as having flat areas with a slope under 5°. The results were validated using a sensitivity analysis model and also compared with those for ground-based stations. Full article
(This article belongs to the Special Issue Geographical Analysis, Urban Modelling, Spatial Statistics, Econometric and Multidimensional Evaluation in Urban Environment)
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23 pages, 3537 KiB  
Article
The Key Factors Driving the Development of New Towns by Mother Cities and Regions: Evidence from China
by Sidong Zhao, Congguo Zhang and Junheng Qi
ISPRS Int. J. Geo-Inf. 2021, 10(4), 223; https://doi.org/10.3390/ijgi10040223 - 5 Apr 2021
Cited by 21 | Viewed by 4165
Abstract
As an important carrier of expanded urban spatial growth, new towns have been a “policy tool” for spatial production in the new era and have received long-term and constant attention from circles such as geography, planning, and economics. National new districts constitute a [...] Read more.
As an important carrier of expanded urban spatial growth, new towns have been a “policy tool” for spatial production in the new era and have received long-term and constant attention from circles such as geography, planning, and economics. National new districts constitute a new regional space for China to implement the national strategy and promote the transformation of urban development mode. They are mutually reinforcing with their mother cities and hinterland provinces. Based on the geodetector method, this paper reveals the key factors driving the development of national new districts by mother cities and hinterland provinces and their interaction effects, which provides a basis for municipal and provincial governments to accurately formulate policies to promote the development of new towns by classification. The study shows that, firstly, there are five types of driving factors, that is, all-round driving factors, scale-increasing factors, expansion and quality-improving factors, expertise driving factors, and non-driving factors. The strength and dimension of the driving factors are characterized by prominent heterogeneity; R&D personnel, export and import trade are the key factors to expand the increment, optimize the inventory, and improve the quality; the overall development driving forces are in the order of innovation > opening > industry > investment > population. Secondly, the pairwise interaction between different factors exhibits two-factor enhancement, and the population shows a nonlinear increase in the driving force of investment, openness, and innovation on a provincial scale. Thirdly, according to the driving force of the factors and the interaction between them, suggestions are put forward based on the development stage and key demands for city and provincial governments to make policies for the development of national new districts, to support the establishment of scientific competition and cooperation between new towns and mother cities or regions, and to build a long-term collaborative development mechanism. Full article
(This article belongs to the Special Issue Geographical Analysis, Urban Modelling, Spatial Statistics, Econometric and Multidimensional Evaluation in Urban Environment)
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19 pages, 16318 KiB  
Article
Estimates of the Ambient Population: Assessing the Utility of Conventional and Novel Data Sources
by Annabel Whipp, Nicolas Malleson, Jonathan Ward and Alison Heppenstall
ISPRS Int. J. Geo-Inf. 2021, 10(3), 131; https://doi.org/10.3390/ijgi10030131 - 3 Mar 2021
Cited by 3 | Viewed by 3243
Abstract
This paper will critically assess the utility of conventional and novel data sources for building fine-scale spatio-temporal estimates of the ambient population. It begins with a review of data sources employed in existing studies of the ambient population, followed by preliminary analysis to [...] Read more.
This paper will critically assess the utility of conventional and novel data sources for building fine-scale spatio-temporal estimates of the ambient population. It begins with a review of data sources employed in existing studies of the ambient population, followed by preliminary analysis to further explore the utility of each dataset. The identification and critiquing of data sources which may be useful for building estimates of the ambient population are novel contributions to the literature. This paper will provide a framework of reference for researchers within urban analytics and other areas where an accurate measurement of the ambient population is required. This work has implications for national and international applications where accurate small area estimates of the ambient population are crucial in the planning and management of urban areas, the development of realistic models and informing policy. This research highlights workday population estimates, in conjunction with footfall camera and Wi-Fi sensors data as potentially valuable for building estimates of the ambient population. Full article
(This article belongs to the Special Issue Geographical Analysis, Urban Modelling, Spatial Statistics, Econometric and Multidimensional Evaluation in Urban Environment)
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21 pages, 17647 KiB  
Article
Radio Base Stations and Electromagnetic Fields: GIS Applications and Models for Identifying Possible Risk Factors and Areas Exposed. Some Exemplifications in Rome
by Cristiano Pesaresi and Davide Pavia
ISPRS Int. J. Geo-Inf. 2021, 10(1), 3; https://doi.org/10.3390/ijgi10010003 - 23 Dec 2020
Cited by 4 | Viewed by 3341
Abstract
This paper—which is contextualized in the discussion on the methodological pluralism and the main topics of medical geography, the complexity theory in geographies of health, the remaking of medical geography and ad hoc systems of data elaboration—focuses on radio base stations (RBSs) as [...] Read more.
This paper—which is contextualized in the discussion on the methodological pluralism and the main topics of medical geography, the complexity theory in geographies of health, the remaking of medical geography and ad hoc systems of data elaboration—focuses on radio base stations (RBSs) as sources of electromagnetic fields, to provide GIS applications and simplifying-prudential models that are able to identify areas that could potentially be exposed to hazard. After highlighting some specific aspects regarding RBSs and their characteristics and summarizing the results of a number of studies concerning the possible effects of electromagnetic fields on health, we have taken an area of north-east Rome with a high population and building density as a case study, and we have provided some methodological and applicative exemplifications for different situations and types of antennas. Through specific functionalities and criteria, drawing inspiration from a precautionary principle, these exemplifications show some particular cases in order to support: possible risk factor identification, surveillance and spatial analysis; correlation analysis between potential risk factors and outbreak of diseases and symptoms; measurement campaigns in heavily exposed areas and buildings; education policies and prevention actions. From an operative viewpoint, we have: conducted some field surveys and recorded data and images with specific geotechnological and geomatics instruments; retraced the routes by geobrowsers and basemaps and harmonized and joined up the materials in a GIS environment; used different functions to define, on aero-satellite images, concentric circular buffer zones starting from each RBS, and geographically and geometrically delimited the connected areas subject to high and different exposure levels; produced digital applications and tested prime three-dimensional models, in addition to a video from a bird’s eye view perspective, able to show the buildings in the different buffer zones and which are subject to a hazard hierarchy due to exposure to an RBS. A similar GIS-based model—reproposable with methodological adjustments to other polluting sources—can make it possible to conceive a dynamic and multiscale digital system functional in terms of strategic planning, decision-making and public health promotion in a performant digital health information system. Full article
(This article belongs to the Special Issue Geographical Analysis, Urban Modelling, Spatial Statistics, Econometric and Multidimensional Evaluation in Urban Environment)
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14 pages, 2222 KiB  
Article
Spatial Patterns of Childhood Obesity Prevalence in Relation to Socioeconomic Factors across England
by Yeran Sun, Xuke Hu, Ying Huang and Ting On Chan
ISPRS Int. J. Geo-Inf. 2020, 9(10), 599; https://doi.org/10.3390/ijgi9100599 - 11 Oct 2020
Cited by 6 | Viewed by 5386
Abstract
To examine to what extent spatial inequalities in childhood obesity are attributable to spatial inequalities in socioeconomic characteristics across a country, we aimed to investigate the spatial associations of socioeconomic characteristics and childhood obesity. We first explored spatial patterns of childhood obesity prevalence, [...] Read more.
To examine to what extent spatial inequalities in childhood obesity are attributable to spatial inequalities in socioeconomic characteristics across a country, we aimed to investigate the spatial associations of socioeconomic characteristics and childhood obesity. We first explored spatial patterns of childhood obesity prevalence, and subsequently investigated the spatial associations of socioeconomic factors and childhood obesity prevalence across England by selecting and estimating appropriate spatial regression models. As the data used are geospatial data, we used two newly developed specifications of spatial regression models to investigate the spatial association of socioeconomic factors and childhood obesity prevalence. As a result, among the two newly developed specifications of spatial regression models, the fast random effects specification of eigenvector spatial filtering (FRES-ESF) model appears to outperform the matrix exponential spatial specification of spatial autoregressive (MESS-SAR) model. Empirical results indicate that positive spatial dependence is found to exist in childhood obesity prevalence across England; and that socioeconomic factors are significantly associated with childhood obesity prevalence across England. In England, children living in areas with lower socioeconomic status are at higher risk of obesity. This study suggests effectively reducing spatial inequalities in socioeconomic status will plays a vital role in mitigating spatial inequalities in childhood obesity prevalence. Full article
(This article belongs to the Special Issue Geographical Analysis, Urban Modelling, Spatial Statistics, Econometric and Multidimensional Evaluation in Urban Environment)
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Review

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23 pages, 2346 KiB  
Review
Using VGI and Social Media Data to Understand Urban Green Space: A Narrative Literature Review
by Nan Cui, Nick Malleson, Victoria Houlden and Alexis Comber
ISPRS Int. J. Geo-Inf. 2021, 10(7), 425; https://doi.org/10.3390/ijgi10070425 - 22 Jun 2021
Cited by 32 | Viewed by 6060
Abstract
Volunteered Geographical Information (VGI) and social media can provide information about real-time perceptions, attitudes and behaviours in urban green space (UGS). This paper reviews the use of VGI and social media data in research examining UGS. The current state of the art is [...] Read more.
Volunteered Geographical Information (VGI) and social media can provide information about real-time perceptions, attitudes and behaviours in urban green space (UGS). This paper reviews the use of VGI and social media data in research examining UGS. The current state of the art is described through the analysis of 177 papers to (1) summarise the characteristics and usage of data from different platforms, (2) provide an overview of the research topics using such data sources, and (3) characterise the research approaches based on data pre-processing, data quality assessment and improvement, data analysis and modelling. A number of important limitations and priorities for future research are identified. The limitations include issues of data acquisition and representativeness, data quality, as well as differences across social media platforms in different study areas such as urban and rural areas. The research priorities include a focus on investigating factors related to physical activities in UGS areas, urban park use and accessibility, the use of data from multiple sources and, where appropriate, making more effective use of personal information. In addition, analysis approaches can be extended to examine the network suggested by social media posts that are shared, re-posted or reacted to and by being combined with textual, image and geographical data to extract more representative information for UGS analysis. Full article
(This article belongs to the Special Issue Geographical Analysis, Urban Modelling, Spatial Statistics, Econometric and Multidimensional Evaluation in Urban Environment)
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Review
Extending Geodemographics Using Data Primitives: A Review and a Methodological Proposal
by Jennie Gray, Lisa Buckner and Alexis Comber
ISPRS Int. J. Geo-Inf. 2021, 10(6), 386; https://doi.org/10.3390/ijgi10060386 - 4 Jun 2021
Cited by 3 | Viewed by 3820
Abstract
This paper reviews geodemographic classifications and developments in contemporary classifications. It develops a critique of current approaches and identifiea a number of key limitations. These include the problems associated with the geodemographic cluster label (few cluster members are typical or have the same [...] Read more.
This paper reviews geodemographic classifications and developments in contemporary classifications. It develops a critique of current approaches and identifiea a number of key limitations. These include the problems associated with the geodemographic cluster label (few cluster members are typical or have the same properties as the cluster centre) and the failure of the static label to describe anything about the underlying neighbourhood processes and dynamics. To address these limitations, this paper proposed a data primitives approach. Data primitives are the fundamental dimensions or measurements that capture the processes of interest. They can be used to describe the current state of an area in a multivariate feature space, and states can be compared over multiple time periods for which data are available, through for example a change vector approach. In this way, emergent social processes, which may be too weak to result in a change in a cluster label, but are nonetheless important signals, can be captured. As states are updated (for example, as new data become available), inferences about different social processes can be made, as well as classification updates if required. State changes can also be used to determine neighbourhood trajectories and to predict or infer future states. A list of data primitives was suggested from a review of the mechanisms driving a number of neighbourhood-level social processes, with the aim of improving the wider understanding of the interaction of complex neighbourhood processes and their effects. A small case study was provided to illustrate the approach. In this way, the methods outlined in this paper suggest a more nuanced approach to geodemographic research, away from a focus on classifications and static data, towards approaches that capture the social dynamics experienced by neighbourhoods. Full article
(This article belongs to the Special Issue Geographical Analysis, Urban Modelling, Spatial Statistics, Econometric and Multidimensional Evaluation in Urban Environment)
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