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Rabiei-Dastjerdi, Hamidreza
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Rabiei-Dastjerdi, Hamidreza
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Rabiei-Dastjerdi, Hamidreza
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Now showing 1 - 6 of 6
- PublicationUrban Consumption Patterns: OpenStreetMap Quality for Social Science ResearchCitizen consumption refers to the goods and services which citizens utilise. This includes time spent on leisure and cultural activities as well as the consumption of necessary and luxury goods and services. The spatial dimension of consumption inequality can show the underlying urban spatial structure and processes of a city. Usually, the main barrier to effectively measuring consumption is the availability and accessibility of spatial data. While the main body of the literature utilises official, government data, such data is not always available, up-to-date or can be costly to acquire. In this paper, we discuss the potential of Volunteered Geographic Information (VGI) as a source of spatial data for determining consumption inequality. To this end, we compared OpenStreetMap (OSM) data, that can be used as proxies for consumption inequality, with official data in the area of Greater London. The results show that OSM is currently inadequate for studying the spatial dimension of consumption. It is our view that while VGI is appropriate for tasks such as routing and navigation, it also has the potential to add value to social science studies in the future.
297 - PublicationCity-region or city? That is the question: modelling sprawl in Isfahan using geospatial data and technology(Springer, 2022-01-04)
; ; ; Urban sprawl is a universal phenomenon and can be seen as a city’s low-density and haphazard development from the centre to suburban areas, and it has different adverse environmental effects at local and regional scales, including increasing the cost of infrastructure. Geospatial data and technology can be used to measure urban sprawl and predict urban expansion. This technology can shed light on the characteristics, causes, and consequences of urban expansion. Unlike other studies, the methodology proposed in this paper works on a regional level rather than an individual city. In this article, Land Use Land Cover changes and the magnitude and direction of city-region sprawl in the Isfahan Metropolitan area were modelled using a multi-temporal analysis of remote sensing imagery. Shannon’s Entropy was used to quantify city-region dispersion during the last fifty years. A Multi-Layer Perceptron Neural Network and Markov Chain Analysis were then used to forecast future city-region sprawl based on past patterns and physical constraints. The results revealed that this region has been suffering from sprawl during this period in different directions. Moreover, it will continue in specific directions due to several economic, political, demographic, environmental, and (urban) planning factors. In addition, the size and speed of city-region sprawl were higher than core city sprawl. The proposed approach can be generalized for other city-regions with a similar spatial structure.Scopus© Citations 7 82 - PublicationThe potential contributions of geographic information science to the study of social determinants of health in IranRecent interest in the social determinants of health (SDOH) and the effects of neighborhood contexts on individual health and well-being has grown exponentially. In this brief communication, we describe recent developments in both analytical perspectives and methods that have opened up new opportunities for researchers interested in exploring neighborhoods and health research within a SDOH framework. We focus specifically on recent advances in geographic information science, statistical methods, and spatial analytical tools. We close with a discussion of how these recent developments have the potential to enhance SDOH research in Iran.
153 - PublicationIdentifying Patterns of Neighbourhood Change Based on Spatiotemporal Analysis of Airbnb Data in DublinIn general, neighbourhoods are susceptible to changes such as economic expansion or decline, new developments and infrastructure, new business and industry, gentrification or super gentrification, decline and abandonment. In this paper, we assess the ability of Airbnb data to identify locations prone to neighbourhood change using data from the Airbnb platform in Dublin, Ireland. Emerging Hotspot Analysis was utilized to identify areas where change is potentially occurring. The results of the analysis were validated by analysing literature about different types of neighbourhood change occurring in Dublin. The results show patterns of change which are occurring in many neighbourhoods in Dublin can be captured by changes in the Airbnb data. The city centre appears to have reachedsaturation point in the volume of Airbnb lettings, while other areas which are undergoing differentforms of Airbnb change are emerging as changing neighbourhoods. This paper shows that Airbnb data has a high potential to reveal underlying socioeconomic processes in the city and also highlights the importance of open access to data for urban studies and monitoring.
351Scopus© Citations 6 - PublicationGap analysis in decision support systems for real-estate in the era of the digital earth(Taylor & Francis, 2021)
; ; ; Searching for a property is inherently a multicriteria spatial decision. The decision is primarily based on three high-level criteria composed of household needs, building facilities, and location characteristics. Location choice is driven by diverse characteristics; including but not limited to environmental factors, access, services, and the socioeconomic status of a neighbourhood. This article aims to identify the gap between theory and practice in presenting information on location choice by using a gap analysis methodology through the development of a seven-factor classification tool and an assessment of international property websites. Despite the availability of digital earth data, the results suggest that real-estate websites are poor at providing sufficient location information to support efficient spatial decision making. Based on a case study in Dublin, Ireland, we find that although neighbourhood digital earth data may be readily available to support decision making, the gap persists. We hypothesise that the reason is two-fold. Firstly, there is a technical challenge to transform location data into usable information. Secondly, the market may not wish to provide location information which can be perceived as negative. We conclude this article with a discussion of critical issues necessary for designing a spatial decision support system for real-estate decision making.Scopus© Citations 20 148 - PublicationSpatial variability of total fertility rate and crude birth rate in a low-fertility country: Patterns and trends in regional and local scale heterogeneity across Italy, 2002–2018(Elsevier, 2020-11)
; ; ; ; Fertility is a key process shaping long-term population dynamics. Distinctive fertility trends have characterized demographic transitions, exhibiting sequential periods of spatial convergence and divergence. This descriptive study investigates the spatiotemporal evolution of Total Fertility Rate (TFR) and Crude Birth Rate (CBR) at different geographical scales in Italy between 2002 and 2018. Descriptive statistics of the TFR and CBR values across geographical scales were computed and the associated maps were prepared for the most detailed spatial levels available; specifically, down to the municipality level. Spatial analysis at the provincial and municipality level was based on both global and local Moran's indexes. Southern Italy, a mostly disadvantaged region, was characterized by relatively stable fertility patterns; fertility then decreased following an opposite trend with respect to economic conditions. The reverse relationship was observed in Northern Italy. As such, economic expansion and recession impacted fertility more intensively in Northern Italy.150Scopus© Citations 19