AN INTEGRATED APPROACH FOR SIMULATION AND PREDICTION OF LAND USE AND LAND COVER CHANGES AND URBAN GROWTH (CASE STUDY: SANANDAJ CITY IN IRAN)

One of the growing areas in the west of Iran is Sanandaj city, the center of Kordestan province, which requires the investigation of the city's growth and the estimation of land degradation. Today, the combination of remote sensing data and spatial models is a useful tool for monitoring and modeling land use and land cover (LULC) changes. In this study, LULC changes and the impact of Sanandaj city growth on land degradation in geographical directions during the period 1989 to 2019 were investigated. Also, the accuracy of three models, artificial neural network-cellular automata (ANN-CA), logistic regression-cellular automata (LR-CA), and the weight of evidence-cellular automata (WOE-CA) for modeling LULC changes was evaluated, and the results of these models were compared with the CA-Markov model. According to the results of the study, ANN-CA, LR-CA, and WOE-CA models, with an accuracy of more than 80%, are efficient and effective for modeling LULC changes and growth of urban areas

Identifying Patterns of Neighbourhood Change Based on Spatiotemporal Analysis of Airbnb Data in Dublin

The 2020 4th International Conference on Smart Grid and Smart Cities (ICSGSC), Osaka, Japan, 18-21 August 2020In 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.European Commission Horizon 20202021-07-01 JG: PDF replaced with correct versio

The potential contributions of geographic information science to the study of social determinants of health in Iran

Recent 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

The potential contributions of geographic information science to the study of social determinants of health in Iran

Recent 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

Urban Consumption Patterns: OpenStreetMap Quality for Social Science Research

The 6th International Conference on Geographical Information Systems Theory, Applications and Management, Online Event, 7-9 May 2020Citizen 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.European Commission Horizon 202

Disclosure and Engagement on Social Media in Iranian Context

People engage with social media platforms for many reasons, and this study investigates how disclosure of different types of information such as about self, others, and non-personal is influenced by privacy concerns and impacts engagement. We focus on Iranian’s culture, because of restrictions put by the government on the use of Internet and social media. We propose that the privacy concerns for self and privacy for others have relationships with self-disclosure and disclosure of information about others. We also investigate the relationship between information disclosure (about self, others, and non-personal) on social media engagement. The survey data collected from Iranian participants (n=379), we empirically tested the proposed research model. We found non-personal sharing has a positive relationship with self-disclosure, disclosure about others, and engagement. Furthermore, results indicate significant negative relationships between privacy concern for self and self-disclosure and privacy concern for others and disclosure about others

Urban Land Use and Land Cover Change Analysis Using Random Forest Classification of Landsat Time Series

Efficient implementation of remote sensing image classification can facilitate the extraction of spatiotemporal information for land use and land cover (LULC) classification. Mapping LULC change can pave the way to investigate the impacts of different socioeconomic and environmental factors on the Earth’s surface. This study presents an algorithm that uses Landsat time-series data to analyze LULC change. We applied the Random Forest (RF) classifier, a robust classification method, in the Google Earth Engine (GEE) using imagery from Landsat 5, 7, and 8 as inputs for the 1985 to 2019 period. We also explored the performance of the pan-sharpening algorithm on Landsat bands besides the impact of different image compositions to produce a high-quality LULC map. We used a statistical pan-sharpening algorithm to increase multispectral Landsat bands’ (Landsat 7–9) spatial resolution from 30 m to 15 m. In addition, we checked the impact of different image compositions based on several spectral indices and other auxiliary data such as digital elevation model (DEM) and land surface temperature (LST) on final classification accuracy based on several spectral indices and other auxiliary data on final classification accuracy. We compared the classification result of our proposed method and the Copernicus Global Land Cover Layers (CGLCL) map to verify the algorithm. The results show that: (1) Using pan-sharpened top-of-atmosphere (TOA) Landsat products can produce more accurate results for classification instead of using surface reflectance (SR) alone; (2) LST and DEM are essential features in classification, and using them can increase final accuracy; (3) the proposed algorithm produced higher accuracy (94.438% overall accuracy (OA), 0.93 for Kappa, and 0.93 for F1-score) than CGLCL map (84.4% OA, 0.79 for Kappa, and 0.50 for F1-score) in 2019; (4) the total agreement between the classification results and the test data exceeds 90% (93.37–97.6%), 0.9 (0.91–0.96), and 0.85 (0.86–0.95) for OA, Kappa values, and F1-score, respectively, which is acceptable in both overall and Kappa accuracy. Moreover, we provide a code repository that allows classifying Landsat 4, 5, 7, and 8 within GEE. This method can be quickly and easily applied to other regions of interest for LULC mapping

Urban Land Use and Land Cover Change Analysis Using Random Forest Classification of Landsat Time Series

Efficient implementation of remote sensing image classification can facilitate the extraction of spatiotemporal information for land use and land cover (LULC) classification. Mapping LULC change can pave the way to investigate the impacts of different socioeconomic and environmental factors on the Earth’s surface. This study presents an algorithm that uses Landsat time-series data to analyze LULC change. We applied the Random Forest (RF) classifier, a robust classification method, in the Google Earth Engine (GEE) using imagery from Landsat 5, 7, and 8 as inputs for the 1985 to 2019 period. We also explored the performance of the pan-sharpening algorithm on Landsat bands besides the impact of different image compositions to produce a high-quality LULC map. We used a statistical pan-sharpening algorithm to increase multispectral Landsat bands’ (Landsat 7–9) spatial resolution from 30 m to 15 m. In addition, we checked the impact of different image compositions based on several spectral indices and other auxiliary data such as digital elevation model (DEM) and land surface temperature (LST) on final classification accuracy based on several spectral indices and other auxiliary data on final classification accuracy. We compared the classification result of our proposed method and the Copernicus Global Land Cover Layers (CGLCL) map to verify the algorithm. The results show that: (1) Using pan-sharpened top-of-atmosphere (TOA) Landsat products can produce more accurate results for classification instead of using surface reflectance (SR) alone; (2) LST and DEM are essential features in classification, and using them can increase final accuracy; (3) the proposed algorithm produced higher accuracy (94.438% overall accuracy (OA), 0.93 for Kappa, and 0.93 for F1-score) than CGLCL map (84.4% OA, 0.79 for Kappa, and 0.50 for F1-score) in 2019; (4) the total agreement between the classification results and the test data exceeds 90% (93.37–97.6%), 0.9 (0.91–0.96), and 0.85 (0.86–0.95) for OA, Kappa values, and F1-score, respectively, which is acceptable in both overall and Kappa accuracy. Moreover, we provide a code repository that allows classifying Landsat 4, 5, 7, and 8 within GEE. This method can be quickly and easily applied to other regions of interest for LULC mapping

City-region or city? That is the question: modelling sprawl in Isfahan using geospatial data and technology

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.European Commission Horizon 2020Update citation details during checkdate report - RO

Gap analysis in decision support systems for real-estate in the era of the digital earth

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.European CommissionEuropean Commission Horizon 202