A new method for multispace analysis of multidimensional social exclusion.

Social phenomena are multidimensional and dependent on geographic space. Numerous methods are capable of representing multidimensional social phenomena through a composite indicator. Among these methods, principal component analysis (PCA) is the most used when considering the geographical perspective. However, the composite indicators built by the method are sensitive to outliers and dependent on the input data, implying informational loss and specific eigenvectors that make multi-space-time comparisons impossible. This research proposes a new method to overcome these problems: the Robust Multispace PCA. The method incorporates the following innovations. The sub-indicators are weighted according to their conceptual importance in the multidimensional phenomenon. The non-compensatory aggregation of these sub-indicators guarantees the function of the weights as of relative importance. Aggregating indicators in dimensions balances the weight structure of dimensions in the composite indicator. A new scale transformation function that eliminates outliers and allows multispatial comparison reduces by 1.52 times the informational loss of the composite indicator of social exclusion in eight cities' urban areas. The Robust Multispace-PCA has a high potential for appropriation by researchers and policymakers, as it is easy to follow, offers more informative and accurate representations of multidimensional social phenomena, and favors the development of policies at multiple geographic scales.

Spatio-temporal variations and contextual factors of the supply of Airbnb in Rome. An initial investigation.

This paper offers an analysis of the supply of Airbnb accommodation in Rome, one of the main tourist destinations in the world, the third-largest city in Europe, by the number of Airbnb listings. The aim is to focus on the recent spatial trend of Airbnb listings, including the period of the COVID-19 pandemic, and highlight the main housing and socioeconomic characteristics of the neighbourhoods associated with a strong presence of Airbnb listings. The study is developed with quantitative methods and spatial regression (spatial lag and spatial error using OLS as a benchmark), based on data collected from the Inside Airbnb and Tomslee websites. In the period 2014-2019, the listing trend in Rome has been increasing in absolute numbers. After the start of the pandemic, the trend became negative, and the decline of Airbnb offerings is more substantial for shared accommodation. Airbnb supply is related to the distance from the city centre, the average income of the area, empty apartments, singles and the share of foreign residents coming from high-income countries. A signal of spatial diffusion of Airbnb listings emerges in the coastal area, even if they are increasingly concentrated in the historic centre, where there is a monoculture of short-term renting.

Emerging paradigm shift in urban indicators: Integration of the vertical dimension.

Since new urbanism strategies encourage higher density and compact city development, it is expected that the height of urban environments will increase in the next few years as a remedy for many urban problems such as urban sprawl, cost of living, and detrimental environmental impacts of horizontal development of cities. Therefore, urban designers and planners should consider the third dimension of cities according to the vertical growth paradigm that is inherently a three-dimensional (3D) socioeconomic and environmental process. While a large body of literature is focusing on horizontal or two-dimensional (2D) urban indicators, it still lacks more research to compare 2D and 3D urban indicators. In this study, urban environment quality indicators, as a prominent example of urban indicators, were measured in two and three dimensions in the central business district of Urmia in Iran. Also, a Pearson's correlation analysis was performed to find a pairwise relationship between indicators. The results of the correlation analysis revealed that most 3D indicators have no significant linear relationship with other indicators, so predicting 3D indicator values based on other indicators is a difficult or even impossible task. Comparing 2D indicators with 3D ones shows that approximately 30 percent of the study area has a different urban environmental quality if it integrates the vertical dimension with 2D indicators. In addition, measuring and modelling 3D indicators provide better locational information on urban conditions and the life of citizens than traditional 2D urban indicators. This study recommends planning for the expansion of 3D information and associated tools that lead to deeper analytical insights into 3D Urban Environmental Quality assessment.

Spatial distribution of regional infrastructures in the northeast of Iran using GIS and Mic Mac observation (A case of Khorasan Razavi province).

Nowadays, recognizing the current situation and forecasting the desired status of spatial analysis of infrastructures regarding security and defense considerations is of great importance. Besides, the use of approaches such as futures studies and its simultaneous application with GIS has the most fundamental contribution to the field of decision-making and appropriate planning method in studies on the spatial defense planning. Accordingly, this paper aims to evaluate the spatial distribution of regional infrastructures in the northeast of Iran using a passive defense approach. In this regard, a descriptive-analytical research methodology, library-documentary studies, and statistical surveys were used in the model framework along with software (Mic Mac and Scenario Wizard) and system analysis (GIS) to achieve the research objective. The statistical population of the study was defined in two human and spatial scales. The entire geographical space of Khorasan Razavi province made the spatial scale. On the human scale, 40 experts (n = 15) and elites (n = 25) in the field of this study were selected as the statistical sample using a purposive non-random model. It is noteworthy that all of the subjects had the required scientific and executive knowledge. According to the total research indicators, the vulnerable zones of the study area could be distinguished into five categories of areas with very high (7.33%), high (16.52%), moderate (29.78%), low (16.94%), and very low (29.4%) vulnerability. Also, according to the results, the density and dispersion patterns of the study area infrastructures were concentrated, clustered, and randomly self-clustered, respectively. In the meantime, factors such as legal, policy, and institutional infrastructure criteria were identified as key drivers influencing the spatial distribution of the province infrastructures. Therefore, it is possible to realize the future models in three scenarios of high desirability (green status), acceptable (yellow status), and crisis (red status). Finally, the paper concludes with some suggestions to increase the desirability of infrastructures in Khorasan Razavi province.

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.