Geographic Information System Tools Research Articles

Solar potential for social benefit: Maps to sustainably address energy poverty utilizing open spatial data in data poor settings

Access to affordable sustainable energy is a significant challenge for many lowincome countries experiencing energy poverty. The United Nations (UN) Sustainable Development Goal (SDG) 7 aims to “ensure access to affordable, reliable, sustainable and modern energy for all.” Simultaneously, the European Union (EU) seeks to reduce emissions by 55 % by 2030. Decarbonization policies must be carefully considered to avoid adverse effects on vulnerable groups experiencing energy poverty. Solar technology offers a viable solution to decarbonize the building sector, reduce energy dependence on fossil fuels, and provide financial benefits to the public. Mapping solar potential is crucial to determine where investment in solar photovoltaic (PV) technology is most advantageous to the populations who stand to benefit the most. We combine a solar potential mapping approach incorporating socio-economic indicators indicative of energy poverty, using off-the-shelf Geographic Information Systems (GIS) tools that are easily replicated across cities facing energy poverty. Utilizing lowest common denominator data and analysis approaches, we offer creative and innovative mapping solutions. The socio-economic factors help to contextualise the benefits of distributed PV systems and highlight the need for mapping solar potential, in combination with energy poverty indicators for sustainable planning and policymaking. Our results for a case study in the city of Plovdiv, Bulgaria, demonstrate high solar energy potential that could meet 29 % of the city's electricity needs, save citizens about M€ 43.84 annually, and pinpoint where to invest first for the highest gains. Finally, we offer suggestions on how to use these results to inform decarbonization policies to benefit low-income populations that are often missed in existing energy policies.

Assessment of Soil Erosion within the Gayathripuzha River Basin Using RUSLE Model and GIS Tools

Soil erosion, a natural phenomenon accelerated by human activities, became a major problem for agricultural land use and for water quality concerns. Soil erosion by water is influenced by various factors like climate, nature of land surface, slope steepness, soil properties, land cover, and management practices.  Estimation of soil erosion is essential to know the nature and quantity of soil loss and create specific strategies for soil conservation. This research paper thoroughly examines the susceptibility of soil erosion in the Gayathripuzha river basin in Kerala, India, which is encompassing a wide area of around 961 km².  Employing the Revised Universal Soil Loss Equation (RUSLE), this investigation assesses the influential factors affecting soil erosion, which comprise rainfall (R Factor), soil erodibility (K Factor), topography (LS Factor), land usage (C Factor), and the impact of conservation measures (P Factor).  The study was conducted using Geographic Information System (GIS) tools like ArcGIS 10.8 Desktop and integrating data on rainfall, soil properties, land topographic features, and land use.  Rainfall erosivity is a major factor causing erosion in Gayathripuzha river basin and it ranges from 746.03 to 1222.35 MJ mm ha−1 h−1 y−1 in the river basin. Soil erodibility factor also varies widely within the region.  Slope length and steepness factor showed that highland ecosystems are present only in a small area of the river basin.  Variation in land use and land cover also influenced the soil erosion within the area. The estimated average annual soil erosion ranges from 0 to 133.18 t ha-1 yr-1 within the research site. The research found that soil erosion varied greatly across the study area. Regions with slopes over 50% had much higher erosion rates, while most of the study area had low erosion levels. Notably, the intensity of soil erosion demonstrated a positive correlation with slope percentage, with areas classified as bare ground demonstrating the highest erosion rates. These findings emphasize the necessity for targeted and comprehensive conservation strategies tailored to the diverse landscape of the region to effectively manage soil erosion and mitigate its adverse impacts.

ANALYSIS OF SUITABLE DUMP SITES IN KAFANCHAN, JEMA’A LOCAL GOVERNMENT AREA KADUNA STATE, NIGERIA

The research focuses on assessing the suitability of dump sites for solid waste disposal in Kafanchan town, Kaduna state, utilizing Surveying and Geo informatics techniques. With the escalating environmental impact of waste due to population growth, urbanization, and industrialization, the study aims to identify appropriate locations for waste disposal. The methodology employs Geographic Information System (GIS) tools, involving the overlaying of datasets and criteria such as drainage, land use, and road networks. The process includes investigating solid waste disposal in Kafanchan, acquiring data through a Global Positioning System (GPS) receiver, converting criteria into GIS layers, and processing the collected information on a computer system using ArcGIS software. The spatial analysis reveals potential sites suitable for solid waste disposal based on connectivity, contiguity, and overlay methods. The results advocate for the effectiveness of GIS in promptly selecting suitable waste disposal sites. The conclusion emphasizes the recommendation to adopt the identified sites for solid waste disposal in the area. Overall, the study underscores the significance of GIS in addressing contemporary environmental challenges related to waste management and highlights the practical application of spatial tools for sustainable solutions.

Storm's influence on long-term shoreline evolution along Casablanca-Mohammedia (Morocco)

Coastal erosion assessment has emerged as one of the most significant instruments for making decisions and offering effective solutions for coastal regions. Mohammedia-Casablanca's shoreline constantly changes due to natural and man-made erosion and accretion. This research uses historical aerial photographs, wave analysis data, geographic information system (GIS) tools, and statistical methods to evaluate spatial and temporal shoreline changes from 1969 to 2020. The analysis of storminess and rates of shoreline change is also discussed jointly in three sediment cells. For this, energy parameters are determined to characterize storm-induced impacts. The Digital Shoreline Analysis System (DSAS) calculated shoreline change using End Point Rate (EPR), Shoreline Change Envelope (SCE), and Net Shoreline Movement (NSM) methods. Out of 2477 transects placed at 30-meter intervals along the study area, approximately 39% of the transects show erosion, and the rest indicate accretion (61%). Over the entire study period (51 years), the analysis of shoreline evolution does not show a clear correlation with storm parameters. The only period in which the shoreline shift correlates with storm activity is 1969–1982. Storms were frequent and intense during this time, and the erosion was at its maximum (over 66% of the coastline). Outside this period, accretion has occurred even during periods of intense storms, as was the case here between 2009 and 2020, when accretion affected 70.1% of the Mohammadia-Casablanca coastline. This suggests that the recent installation of marine structures (groynes, seawalls, dikes, jetties, and piers) multiplied after 1982 has positively affected the rate of coastal erosion.

3D Point Cloud and GIS Approach to Assess Street Physical Attributes

The present research explores an innovative approach to objectively assessing urban streets attributes using 3D point clouds and Geographic Information Systems (GIS). Urban streets are vital components of cities, playing a significant role in the lives of their residents. Usually, the evaluation of some of their physical attributes has been subjective, but this study leverages 3D point clouds and digital terrain models (DTM) to provide a more objective perspective. This article undertakes a micro-urban analysis of basic physical characteristics (slope, width, and human scale) of a representative street in the historic centre of Valencia (Spain), utilizing 3D laser-scanned point clouds and GIS tools. Applying the proposed methodology, thematic maps were generated, facilitating the objective identification of areas with physical attributes more conducive to suitable pedestrian dynamics. This approach provides a comprehensive understanding of urban street attributes, emphasizing the importance of addressing their assessment through advanced digital technologies. Moreover, this versatile methodology has diverse applications, contributing to social sustainability by enhancing the quality of urban streets and open spaces.

Road infrastructure maintenance: Operative method for interventions’ ranking

Planned maintenance of transportation infrastructure is a milestone in maintaining the functionality and safety of road networks. This paper is part of this process and aims to propose an easily applicable and versatile operational methodology for prioritizing maintenance interventions generally scheduled on road networks of considerable extent. An operational methodology for ranking and prioritizing maintenance interventions is proposed; this method is applicable to road networks of such size that direct inspection of each situation is impossible. The core of the paper is the analytical description of this methodology, based on a weighted sum function of three blocks: i) Category − is a score related to the purpose of the intervention; ii) Asset − is a score due to the road on which the intervention is located (considering Average Daily Traffic, Accidentality, Social Cost, Road Type, Routes); iii) Typology − is a score related to the element on which the intervention is being carried out. After describing the parameters and the implementation procedure for each block, the calibration of the relative weights is described, and a sensitivity analysis is performed. In the case study, the proposed methodology is applied to the ANAS corporate network in Italy. This case study will highlight the usefulness, versatility, and operability of the methodology with GIS (Geographic Information System) tools for implementing thematic maps. A concluding section concerns the addition of a scoring block − Spatial Context − as an additional differentiating factor due to the spatial context of each road.

Detection of karst depression in Brazil comparing different semantic and instance segmentations and global digital elevation models

This research assessed the efficacy of deep segmentation in identifying and measuring natural karst depressions in the Bambuí Group's carbonate rocks in Western Bahia, Brazil. The investigation used five Global Digital Elevation Models (DEMs) with 30-m resolution: Advanced Spaceborne Thermal Emission and Reflection Radiometer - Global Digital Elevation Model version 3 (ASTER-GDEM v3), Advanced Land Observing Satellite World 3D – 30 m version 3.2 (AW3D30 v3.2), Copernicus 30 m global DEM (GLO-30), NASA Digital Elevation Model version 1 (NASADEM v1), and Shuttle Radar Topography Mission version 3 (SRTM v3). The karst feature detection analysis compared five semantic segmentation architectures: Feature Pyramid Networks (FPN), LinkNet, UNet, UNet++, DVL3+, using an EfficientNet-B7 backbone, one instance segmentation model (Mask-RCNN), and a semantic-to-instance conversion method. This comparison considers different datasets, including one, two, or eleven variables (DEM, DEM-based sink depth, and nine terrain attributes). Besides, we performed a combinatorial analysis of the DEMs to evaluate the improvement in karst feature detection. The methodology involved the generation of geomorphometric attributes, sample labeling from Sentinel-2 and Operational Land Imager-Landsat 8 datasets, training-validation-testing steps with 128 × 128 samples, reconstruction of large images for semantic and instance segmentation, and accuracy analysis. The findings revealed that GLO-30 and AW3D30 data were the most accurate, while ASTER GDEM performed poorly in both segmentation forms. Among the semantic models, FPN showed the highest accuracy. The 11-variable models preferentially outperformed those with fewer in both types of segmentation. The approach of semantic-to-instance conversion with Geographic Information System tools favored individualizing karst depressions and obtaining efficient quantification, consisting of an easy alternative to achieve instance segmentation. Reconstruction of large remote sensing images through sliding windows considering the specifics of instance and semantic segmentation demonstrated that smaller stride enhances object coverage and reduces the risk of losing crucial information, effectively improving the predictions. The combinatorial analysis for semantic and instance segmentation indicates that models incorporating more DEMs (4- and 5-DEM models) and variables achieve generally higher accuracy. The best result in semantic segmentation was combining the five DEM datasets using 11 variables, reaching an F-score of 85.06 and IoU of 74.00. In instance segmentation, the best result for the bounding box was the model that integrates AW3D30, GLO-30, NASADEM, and SRTM with eleven variables reaching Average Precision (AP) of 45.64, AP50 of 88.40 and AP75 of 39.38, while the best result for the segmentation mask was the model that integrates the five models with eleven variables with AP of 42.61, AP50 of 86.79 and AP75 of 35.43. These results demonstrate that integrating a wide range of DEM data, even the lowest performing ones, improves model generalization and accuracy in segmentation, leveraging strengths and mitigating individual weaknesses. Future work could explore high-resolution DEMs and the integration of various deep-learning methods.

Morphometric Analysis Using Geographical Information System and the Relationship with Precipitation Quantiles of Major Dam Basins in South Korea

Geographic information system (GIS) and remote sensing (RS) technologies are potent tools for evaluating various aspects of basin hydrology. This study conducted a morphological analysis using GIS tools on seven major dam basins in Korea. Additionally, long-term RS-based precipitation data were obtained, the probability of precipitation was estimated, and their relationship was examined. The findings are summarized as follows. It was observed that most major dam basins in Korea, which were the focus of this study, had a broad radial shape, and due to the mountainous topography, there was a notable presence of numerous river branches. Through the estimation of probability precipitation and its comparison with morphological indices, it was noted that wider basins tend to have higher rainfall amounts and a relatively uniform spatial distribution. Furthermore, it was found that the more uniform the spatial distribution, the simpler the river network. This trend becomes more pronounced in relation to basin size for longer durations, and in spatial dispersion for shorter durations. However, it is crucial to acknowledge that the complexity of these relationships is also affected by other factors such as climate, altitude, and local geographical conditions.

Socio-environmental and technical factors assessment of photovoltaic hydrogen production in Antofagasta, Chile

This study introduces a method for identifying territories ideal for establishing photovoltaic (PV) plants for green hydrogen (GH2) production in the Antofagasta region of northern Chile, a location celebrated for its outstanding solar energy potential. Assessing the viability of PV plant installation necessitates a balanced consideration of technical aspects and socio-environmental constraints, such as the proximity to areas of ecological importance and indigenous communities, to identify potential zones for solar and non-conventional renewable energy (NCRE)-based hydrogen production.To tackle this challenge, we propose a methodology that utilizes geospatial analysis, integrating Geographic Information System (GIS) tools with sensitivity analysis, to determine the most suitable sites for PV plant installation in the Antofagasta region. Our geospatial analysis employs the QGIS software to identify these optimal locations, while sensitivity analysis uses the Sørensen–Dice coefficient method to assess the similarity among chosen socio-environmental variables.Applying this methodology to the Antofagasta region reveals that a significant area within a 15 km radius of existing road networks and electrical substations is favorable for photovoltaic projects. Our sensitivity analysis further highlights the limiting effects of socio-environmental factors and their interactions. Moreover, our research finds that enlarging areas of socio-environmental importance could increase the total hydrogen production by about 10% per commune, indicating the impact of these factors on the potential for renewable energy production.

Regional Analysis of Gender Inequality in the Kazakh Labor Market in the Context of Sustainable Development

Kazakhstan demonstrates its commitment to promoting gender equality and works closely with the global community. Over the past decade, the gender gap in the structure of the labor force of the Republic of Kazakhstan has narrowed slightly. However, gender gaps persist. Women in Kazakhstan make up slightly more than half of the country’s total population. Still, their contribution to the republic’s economic performance, growth, and well-being is significantly below its potential. Access to the Republic’s gender statistics is essential and critical for studying and tracking changes in gender inequality. This article examines the gender dynamics of the labor market of the Republic of Kazakhstan at the macro and micro levels using a geographic information system (GIS). GIS tools provide display and analysis capabilities that are underutilized in workforce development, monitoring, and management. In this study, the authors tried to model spatial indicators of gender inequality in the labor market of the Republic of Kazakhstan. The study results show that the employment rate and the level of women’s wages are primarily related to the gender segregation of vocational education, which lays the foundation for sectoral differentiation of employment with different geographical distributions. The greater competitiveness of women in the labor market of Kazakhstan is also hampered by the so-called “glass ceiling” or low accessibility to leadership positions. The wage gap between men and women in the republic has remained unchanged for a long time and shows no signs of reduction. In addition, such an important element of the labor market as unpaid domestic work, which is carried out mainly by women, remains unrecognized.

Technoeconomic Analysis of a Wind Power Generation System and Wind Resource Mapping Using GIS Tools: The Case of Twelve Locations in the Commune of Evodoula, Cameroon

This paper performs a technoeconomic analysis of the wind power potential and evaluates the cost of wind power generation in Evodoula, comparing two methods, the conventional method and the uncertainty method based on a comparative spatiotemporal approach using the geographic information system (GIS) software tool. This study is based on satellite wind data measured at 10 m above ground level (AGL) over a 40-year period (1980-2019), by the meteorological service NASA (National Aeronautics and Space Administration)/Goddard Space Flight Center (GSFC). The main objectives are to obtain an appropriate design for a proven optimal location and to assess the viability of wind power at Evodoula. For this type of study, there is little literature available. The optimization of an onshore wind farm and deployment in the wind energy development interest area (ZIDEE) is carried out to obtain a minimum and parsimonious discounted cost production unitary (CPU) of the electricity produced. The results showed that in Okok, a location with a large energy deficit, an onshore wind farm with an electricity generation capacity of 12.5 MW with 5 NORDEX N100-2.5MW wind turbines would have a total energy production (TEP) of about 64.0254825 TWh and a selling price of electricity that would be 0.0034 CAD$/kWh, which is very low compared to the utility price (about 98% cheaper). The total cost of the wind farm would be about $11 million, with a net present cost of about $218 million. The annual profit generated by the wind farm would be over $6 billion. The return on investment (ROI) of the project is estimated at 2880.882%. The constructed onshore wind farm would avoid CO2 emission at over 11 MtCO2,eq/year as the energy generated is from the atmosphere. The wind farm would realize an average annual cash flow estimated at nearly $30 million after 20 years of operation. These savings would allow the installation of CO2 capture systems in conventional power plants. In addition, the analysis of uncertainties and risks was identified and quantified to estimate the confidence levels of the project development results. The risks have been assessed, and we recommend that the total uncertainty of the project is around 15%. The energy values in P75 and P90 are 10.08% and 19.22%, respectively, lower than the energy value in P50 of 11.60 GWh/An. Finally, the main policy recommendations for an inclusive design process are highlighted. The contribution of this study is to assist policy makers in making appropriate decisions in the development and implementation of energy and environmental policy in Cameroon and in many continental areas.

Determination of the Groundwater Potential Zones in Babylon Using Remote Sensing & GIS Techniques

Freshwater found underground in the tiny spaces (pores) of soil and rocks is known as groundwater. Groundwater is a valuable resource that considerably contributes to the yearly supplies. To preserve water quality and oversee groundwater systems, evaluation of the potential zone of groundwater recharge is crucial. Using remote sensing and Geographic Information System (GIS) tools, groundwater potential zones are identified. This study proposes to use satellite imagery and the software program (ArcGIS) to find a groundwater potential zone in Babylon City, Iraq. A groundwater map has been created using various data, namely slope, drainage density, lineament density, land use/land cover, geology type, soil type, and rainfall map. All these data are collected using a weighted overlay tool. Five categories were created based on the groundwater results: very poor, poor, moderate, good, and exceptional. The 'good' class covers about 32% of the study area. The current study shows that excellent groundwater class exists in Al-Hindiyah Dam, Al-Muhaweel, Al-Midhatiyah, Al-Kifil, and Al-Mashrua’a. The excellent groundwater class in Al-Shommali covers approximately 198.0054 km2. Certain areas like Al-Musaib Center, Al-Hilla Center, Al-Hashimiyah, and Abi Garaq don't have much groundwater. The moderate and excellent classes cover around 19% of the study area. The main conclusion is that Remote Sensing and GIS have gained recognition as a practical approach to mapping groundwater potential zones due to their ability to integrate diverse spatial data.

NETWORK ANALYSIS FOR THE STUDY OF TRANSPORT IN THE METROPOLITAN AREA OF ALGEIRS

Transport in the metropolitan area of Algiers is very complicated, so we used the powerful tools of geographic information systems in the analysis of transport in the study area. Through the utilization of network analysis and GIS capabilities, our study focuses on optimizing transportation networks and enhancing port accessibility in the metropolitan regions of Boumerdes, Blida, Tipaza, and Algiers. Our methodology involves data collection, GIS analysis, and mapping to generate databases and maps of metropolitan networks. Through assessing port accessibility, road connectivity, and population-based hotspots analysis. Our study reveals Algiers as the most accessible municipality, attributed to its strong centralization. The roadbased analysis and population-based hotspot analysis provide valuable insights into port accessibility, road connectivity, and high-density townships, facilitating informed urban planning and infrastructure development.

Implementing policies to mitigate urban heat islands: Analyzing urban development factors with an innovative machine learning approach

Taiwan is located in a hot and humid subtropical climate. Urban development, building heat emissions, and human activities lead to the urban heat island (UHI) effect in which urban air temperatures are significantly higher than those in the surrounding suburban areas. In this study, the factors contributing to the UHI effect were investigated in a case study of Taichung City, Taiwan by using high-resolution geographic climate data. Geographic Information System tools were used for grid-based spatial data analysis, and a physiological equivalent temperature index map was calculated and overlaid to identify heat zones. The results revealed that environmental factors have complex, nonlinear effects on temperatures in in urban areas; hence, the analysis was challenging. The decision tree algorithm was used to identified key environmental factors contributing to urban heat in areas with different levels of development; a weighting analysis was then performed to select effective urban cooling strategies. Finally, backpropagation neural network models tailored to different development levels were used for simulations to validate the proposed cooling policies. The results of this study were incorporated into the urban planning policies of Taichung City, Taiwan to mitigate the UHI effect and create a more suitable urban living environment.

Ghost Gears in the Gulf of Gabès: Alarming Situation and Sustainable Solution Perspectives

This study represents a first step in investigating the prevalence, causes, and spatial distribution of Abandoned, Lost, or Discarded Fishing Gears (ALDFGs) in the Gulf of Gabès, a critical fishing area in Tunisia. Five main fishing gear types—benthic trawls, encircling nets, longlines, gillnets, and trammel nets were analysed for their loss rates and contributing factors. The study involved extensive surveys among local fishermen and the use of geographic information system (GIS) tools for spatial mapping. The findings reveal varying loss rates among different gear types, with longlines experiencing the highest losses (59%), followed by trammel nets (45%), gillnets (40%), trawls (38%), and encircling nets (36%). Causes of gear loss include entanglement on obstructions, conflicts with other fishing gears, entanglement with marine animals, adverse weather conditions, and intentional dumping at sea. The study highlights the challenges posed by the consistent increase in fishing effort in the region. Spatial distribution mapping indicates two significant concentrations of ALDFGs, correlating with specific fishing activities and depths. The study emphasizes the importance of addressing gear losses as a threat to marine biodiversity and ecosystem health. Recommendations for mitigation include improving boat equipment, raising awareness among fishermen, implementing effective waste management, and exploring economic incentives for ALDFG recovery. This research contributes essential insights for fisheries management, environmental conservation, and sustainable practices in the Gulf of Gabès. Collaborative efforts are crucial for developing and implementing strategies that minimize the impact of ALDFGs on the marine environment while supporting the socio-economic interests of fishermen.

Assessing wildfire exposure and social vulnerability at the local scale using a GIS-based approach

Exposure and vulnerability analysis are valuable tools for wildfire management, especially important for local communities that suffer from very destructive events and that require mitigation approaches adjusted to their abilities and needs. We present a methodological procedure to analyze wildfire exposure levels, social vulnerability conditions and coping capacity at the local scale, for villages or small human settlements. The procedure was developed using GIS (Geographic Information Systems) and programming tools in R and Python, which can be adapted and updated depending on the data available. The development of accessible procedures and easily replicable methodologies facilitates knowledge transfer and supports the application of mitigation and adaptation strategies, tailored to the conditions of the exposed areas.•A step-by-step procedure for the assessment of Exposure, Vulnerability, and Coping Capacity, using Python and R programming language.•Automated processes, easily replicable and adjustable to other areas.•Indications for adapting the methodology using European/international databases.

GIS TOOLS IN THE CONSERVATION AND SUSTAINABLE DEVELOPMENT NATIONAL PARKS, FORESTS AND RURAL AREAS

The problem of optimising the location of crops/plots in protected areas, national parks, farmland or forests involves trying to reconcile their proper functioning with nature conservation, the preservation of unique ecosystems or sustainable development. Such areas are crucial for the preservation of biodiversity and wildlife. This paper examines and discusses the impact of land consolidation in rural areas using GIS (Geographic Information System) tools. It performs a series of network analyses to determine the real distances (following the road network) and linear distances (rectilinear distances) between habitats and farmland. A tool was also developed to automate this process. The results obtained were visualised using map compositions, tables and graphs. The implemented project had a significant beneficial effect on the change in the land structure in the analysed village. The number of registered plots decreased by 40% (from 1,189 to 711) due to the consolidation process.

Use of GIS and RS Tools for Analysis of Land Use Land Cover and Land Surface Temperature: A Case Study of District Khairpur, Pakistan

Land use and land cover (LULC) monitoring plays an important role in sustainable planning and management of resources. This research focuses on analysing historical LULC changes over the Khairpur district of Sindh province of Pakistan. Remote Sensing and Geographic Information System (RS & GIS) tools have been employed using the maximum likelihood algorithm of the supervised classification method. Temporal data of the latest high-resolution satellite imagery provided by NASA GLCF (Global Land Cover Facility), Landsat 8 Operational Land Imager (OLI), and Landsat 4, 5 Thematic Mapper (TM) for 2001, 2010, 2015, and 2020 were acquired from Earth Explorer of United States Geological Survey (USGS). The study area is classified into four major classes namely Barren Land, Vegetation, Built-up areas, and Water Bodies. Analysis revealed that for 2001 and 2005 there was no change in barren land area, whereas in 2010 slight decrease in barren land was found which further decreased for the analysis period of 2020, overall barren land class decreased at a rate of 07% (i.e., 74% to 67%) during the analysis period. Also, vegetation and water bodies classes experienced variations throughout the analysis period, however built-up areas showed constant increase throughout the analysis period where built-up area classes increased from 9% to 15%. Whereas land surface temperature (LST) throughout the analysis period showed increasing trends, for both high ranges and low ranges. It was analysed that the areas having dominancy of the barren land class experienced the highest temperature ranges whereas low temperature ranges were discernible in the areas having vegetation covers. It can be concluded from the study that barren land covers the major area of the study area and there is an increase in built-up areas which can further enhance the LST in the study area.

The Effect of Revitalisation on the Value of Residential Premises in Historic Urban Layouts

Abstract The aim of this research was to analyze the effects of revitalization activities performed in a selected historical urban layout using the mixed method in which statistical analysis was supported by Geographic Information System (GIS) tools on the price level of residential premises on the secondary market. The subject of the analyses is “Old” Nowa Huta (Krakow, Poland), which was designed as an ideal independent city in the mid-20th century. The analysis covered the period from January 1, 2007 to December 31, 2020. The conducted analyses have demonstrated that the transaction prices (and hence the value) of residential premises located in “Old” Nowa Huta increased during the performed revitalization activities. However, based on the results obtained, it could not be confirmed that this was the direct result of the revitalization itself, because the price level within the research site increased in proportion to the rising prices in the entire city. It seems, however, that although the changes in the quality of space in the analyzed area are not spectacular, they serve to improve the quality of life of the local community and are one of the ways to protect this historic urban layout.

Urban planning impact on summer human thermal comfort in Doha, Qatar

Urbanization entails extensive construction and substantial land use alterations, converting natural areas into residential, commercial, mixed-use and industrial. These alterations disrupt the land surface energy, impacting the land surface temperatures (LSTs). Elevated LSTs affect the thermal comfort of urban residents exerting pressure on the environment and ecosystems. This study investigates the repercussions of elevated LST on human thermal comfort, focusing on Doha municipality in Qatar for 2002–2003, 2013, and 2022, with a specific focus on summer temperatures. Utilizing remote sensing and Geographical Information Systems (GIS), we conducted an in-depth investigation, employing Landsat data along and GIS tools to create land use maps and LST pattern maps and establish a correlation between land use and LST. The findings reveal a notable increase in the built-up area of Doha municipality, predominantly at the expense of desert land and water bodies. The LST increases consistently by 0.65 °C annually, shifting human thermal comfort from "moderate" to "heat stress" from 2002 to 2013 and 2023 in all neighborhoods of Doha. Our comparison of LST between different urban areas indicates lower temperatures near water bodies and suburban developments and higher temperatures in downtown areas. Suburban developments exhibit a favourable impact in desert areas compared to compact developments. Our methodology of combining LST from remote sensing data with thermal comfort indices for more comprehensive and accurate estimation of outdoor thermal comfort can be applied in various geographies.