Mapping urban well-being with Quality Of Life Index (QOLI) at the fine-scale of grid data.

Accessibility of transport infrastructure, commercial amenities, recreational facilities, and green spaces is widely recognised as crucial to the well-being of urban residents. However, these features are often unevenly distributed across the geographical boundaries of a city, leading to disparities in the local quality of life. This study focuses on the city of Warsaw, Poland, and uses the aforementioned characteristics and the framework of the '15-min city' concept to construct a grid-level urban Quality of Life Index (QOLI) that facilitates comparisons between the city's districts and local neighbourhoods. The results of our study reveal a "high-inside, low-outside" pattern of quality of life, characterised by higher standards of living in the central districts and lower standards at the city's periphery.

Fresh air in the city: the impact of air pollution on the pricing of real estate.

This study investigates the impact of ambient air pollution on housing prices in Warsaw, Poland, by examining spatial dependencies. The high concentration of particulate matter (PM10, PM2.5 and PM1) is expected to reduce real estate values. Using a hedonic model with approximately 15,000 observations and a spatial error model, we did not find evidence of this impact. Standard and premium housing submarkets differ in price determinants, but both are insensitive to environmental issues. This could be explained by the lack of comprehensive intra-urban historical information on air pollution, which limits investors' rationality and their ability to properly value real estate based on environmental issues. Additionally, measurement and aggregation issues, along with low pollution variability within the city, may contribute to the insignificance of this information in real estate sales prices. Our empirical research confirms a strong link between air pollution and weather conditions within the city, where low temperatures and low-speed southern winds worsen contamination levels, while high temperatures and westerly winds improve air quality. Furthermore, we find that incorporating pollution data using PM yearly mean concentration works better in modelling than the PCA-reduced air pollution index.

Natural spatial pattern-When mutual socio-geo distances between cities follow Benford's law.

Benford's law states that the first digits of numbers in any natural dataset appear with defined frequencies. Pioneering, we use Benford distribution to analyse the geo-location of cities and their population in the majority of countries. We use distances in three dimensions: 1D between the population values, 2D between the cities, based on geo-coordinates of location, 3D between cities' location and population, which jointly reflects separation and mass of urban locations. We get four main findings. Firstly, we empirically show that mutual 3D socio-geo distances between cities and populations in most countries conform with Benford's law, and thus the urban geo-locations have natural spatial distribution. Secondly, we show empirically that the population of cities within countries follows the composition of gamma (1,1) distributions and that 1D distance between populations also conforms to Benford's law. Thirdly, we pioneer in replicating spatial natural distribution-we discover in simulation that a mixture of three pure point-patterns: clustered, ordered and random in proportions 15:3:2 makes the 2D spatial distribution Benford-like. Complex 3D Benford-like patterns can be built upon 2D (spatial) Benford distribution and gamma (1,1) distribution of cities' sizes. This finding enables generating 2D and 3D Benford distributions, which may replicate well the urban settlement. Fourth, we use historical settlement analysis to claim that the geo-location of cities and inhabitants worldwide followed the evolutionary process, resulting in natural Benford-like spatial distribution and to justify our statistical findings. Those results are very novel. This study develops new spatial distribution to simulate natural locations. It shows that evolutionary settlement patterns resulted in the natural location of cities, and historical distortions in urbanisation, even if persistent till now, are being evolutionary corrected.

Spatial and Machine Learning Approach to Model Childhood Stunting in Pakistan: Role of Socio-Economic and Environmental Factors.

This study presents the determinants of childhood stunting as the consequence of child malnutrition. We checked two groups of factors-the socio-economic situation and climate vulnerability-using disaggregated sub-regional data in the spatial context. Data related to the percentage of stunted children in Pakistan for 2017 were retrieved from MICS 2017-18 along with other features. We used three quantitative models: ordinary least squares regression (OLS) to examine the linear relationships among the selected features, spatial regression (SDEM) to identify and capture the spatial spillover effect, and the Extreme Gradient Boosting machine learning algorithm (XGBoost) to analyse the importance of spatial lag and generate predictions. The results showed a high degree of spatial clustering in childhood stunting at the sub-regional level. We found that a 1 percentage point (p.p.) increase in multi-dimensional poverty may translate into a 0.18 p.p. increase in childhood stunting. Furthermore, high climate vulnerability and common marriages before age 15 each exacerbated childhood stunting by another 1 p.p. On the contrary, high female literacy and their high exposure to mass media, together with low climate vulnerability, may reduce childhood stunting. Model diagnostics showed that the SDEM outperformed the OLS model, as AICOLS = 766 > AICSDEM = 760. Furthermore, XGBoost generated the most accurate predictions in comparison to OLS and SDEM, having the lowest root-mean-square error (RMSE).

Decoding Rocks: An Assessment of Geomaterial Microstructure Using X-ray Microtomography, Image Analysis and Multivariate Statistics.

An understanding of the microstructure of geomaterials such as rocks is fundamental in the evaluation of their functional properties, as well as the decryption of their geological history. We present a semi-automated statistical protocol for a complex 3D characterization of the microstructure of granular materials, including the clustering of grains and a description of their chemical composition, size, shape, and spatial properties with 44 unique parameters. The approach consists of an X-ray microtomographic image processing procedure, followed by measurements using image analysis and statistical multivariate analysis of its results utilizing freeware and widely available software. The statistical approach proposed was tested out on a sandstone sample with hidden and localized deformational microstructures. The grains were clustered into distinctive groups covering different compositional and geometrical features of the sample's granular framework. The grains are pervasively and evenly distributed within the analysed sample. The spatial arrangement of grains in particular clusters is well organized and shows a directional trend referring to both microstructures. The methodological approach can be applied to any other rock type and enables the tracking of microstructural trends in grains arrangement.