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This book provides an up-to-date overview of research being done in the field of spatial data quality, which looks at understanding, measuring, describing, and communicating information about the imperfections of geographic data used by GIS and other mapping software. It presents results from a number of current research projects in this area, from the assessment of data accuracy to legal aspects relating to the quality of geographic information.--From publisher's description.

This book explains the concept of spatial data quality, a key theory for minimizing the risks of data misuse in a specific decision-making context. Drawing together chapters written by authors who are specialists in their particular field, it provides both the data producer and the data user perspectives on how to evaluate the quality of vector or raster data which are both produced and used. It also covers the key concepts in this field, such as: how to describe the quality of vector or raster data; how to enhance this quality; how to evaluate and document it, using methods such as metadata; how to communicate it to users; and how to relate it with the decision-making process. Also included is a Foreword written by Professor Michael F. Goodchild.

This research was carried out to assess the concentrations of carbon monoxide (CO) and formaldehyde (HCHO) in Edo State, Southern Nigeria, using remote sensing data. A secondary data collection method was used for the assessment, and the levels of CO and HCHO were extracted annually from Google Earth Engine using information from Sentinel-5-P satellite data (COPERNISCUS/S5P/NRTI/L3_) and processed using ArcMap, Google Earth Engine, and Microsoft Excel to determine the levels of CO and HCHO in the study area from 2018 to 2023. The geometry of the study location is highlighted, saved and run, and a raster imagery file of the study area is generated after the task has been completed with a ‘projection and extent’ in the Geographic Tagged Image File Format (.tiff) and downloaded from the Google Drive and saved into folders, imported into the ArcMap for data processing and Excel worksheet for analysis. The raster data were collected annually for each pollutant with the ‘filterDate = year-01–01; year-12–31’. Results showed that the annual mean concentrations of CO ranged from ‘4.67 × 10 −2  mol/m 2 ’ to ‘5.34 × 10 −2  mol/m 2 ’. The maximum concentration was found in the year 2018 and the minimum was found in the year 2023, a relatively high concentration of CO may lead to the formation of carboxyhaemoglobin which decreases the capacity of the blood to transport oxygen causing lung cancer, heart problems, respiratory conditions and damage to other organs. While the annual mean concentrations of HCHO ranged from ‘1.89 × 10 −4  mol/m 2 ’ to ‘2.18 × 10 −4  mol/m 2 ’, the maximum concentration was found in the year 2021 and the minimum was found in the year 2019, increasing concentration of HCHO may be due to biomass burning and the combustion of methane (CH 4 gas), and can cause nasopharyngeal cancer in humans. Based on the result of this study, constant monitoring of the air quality and atmospheric pollutants to ensure early detection of a decrease or increase in the concentration of atmospheric pollutants, implementation of air pollution control policies, spatial data collection, air quality modelling, hotspot identification and source distribution using the geographic information system (GIS), promotion of cleaner technologies, including the use of low-emission vehicles and renewable energy sources, public awareness and education on the impact of atmospheric pollutants and the human contributions to the increasing production of atmospheric pollutants are highly recommended.

Groundwater management decisions require robust methods that allow accurate predictive modeling of pollutant occurrences. In this study, random forest regression (RFR) was used for modeling groundwater nitrate contamination at the African continent scale. When compared to more conventional techniques, key advantages of RFR include its nonparametric nature, its high predictive accuracy, and its capability to determine variable importance. The latter can be used to better understand the individual role and the combined effect of explanatory variables in a predictive model. In the absence of a systematic groundwater monitoring program at the African continent scale, the study used the groundwater nitrate contamination database for the continent obtained from a meta-analysis to test the modeling approach; 250 groundwater nitrate pollution studies from the African continent were compiled using the literature data. A geographic information system database of 13 spatial attributes was collected, related to land use, soil type, hydrogeology, topography, climatology, type of region, and nitrogen fertilizer application rate, and these were assigned as predictors. The RFR performance was evaluated in comparison to the multiple linear regression (MLR) methods. By using RFR, it was possible to establish which explanatory variables influence the occurrence of nitrate pollution in groundwater (population density, rainfall, recharge, etc.). Both the RFR and MLR techniques identified population density as the most important variable explaining reported nitrate contamination. However, RFR has a much higher predictive power (R2 = 0.97) than a traditional linear regression model (R2 = 0.64). RFR is therefore considered a very promising technique for large-scale modeling of groundwater nitrate pollution.

It is more than 4 years since the 2030 agenda for sustainable development was adopted by the United Nations and its member states in September 2015. Several efforts are being made by member countries to contribute towards achieving the 17 Sustainable Development Goals (SDGs). The progress which had been made over time in achieving SDGs can be monitored by measuring a set of quantifiable indicators for each of the goals. It has been seen that geospatial information plays a significant role in measuring some of the targets, hence it is relevant in the implementation of SDGs and monitoring of their progress. Synoptic view and repetitive coverage of the Earth’s features and phenomenon by different satellites is a powerful and propitious technological advancement. The paper reviews robustness of Earth Observation data for continuous planning, monitoring, and evaluation of SDGs. The scientific world has made commendable progress by providing geospatial data at various spatial, spectral, radiometric, and temporal resolutions enabling usage of the data for various applications. This paper also reviews the application of big data from earth observation and citizen science data to implement SDGs with a multi-disciplinary approach. It covers literature from various academic landscapes utilizing geospatial data for mapping, monitoring, and evaluating the earth’s features and phenomena as it establishes the basis of its utilization for the achievement of the SDGs.

Sustainable sources like wind, solar, and geothermal power are defined as a clean source of renewable energy which has a less harmful impact on the environment than other energy sources such as coal, natural gas and oil. Turkey is one of the energy-importing countries where air pollution has been become an inevitable environmental concern. Thus, investments on sustainable sources have been developed rapidly in recent years in Turkey. This paves the way for studying a site selection problem considering both solar and wind energy in Igdir Province located in the east part of Turkey. In the literature, there are many studies on solar-wind energy to select a desirable site for both energy sources, and many solution techniques have been proposed dealing with this problem. In this study, one of multi-criteria decision-making methods named analytical hierarchy process (AHP) and geographical information systems (GIS) are used to determine suitable site selection for solar-wind energy investigating four counties of Igdir: Tuzluca, Igdir Central, Karakoyunlu and Aralik. The aim of this work is first to investigate possible locations for solar-wind power plant installation using a mapping method, GIS, and then, AHP is applied to the problem to obtain optimum areas for both solar-wind energy. Also, more accurate results are provided comparing results of two methods, GIS and AHP. The results reveal that 524.5 km 2 for solar power plant and 147.2 km 2 for wind turbine are suitable while only 49.1 km 2 is suitable for solar-wind power plan installation.

Historical topographical maps contain valuable, spatially and thematically detailed information about past landscapes. Yet, for analyses of landscape dynamics through geographical information systems, it is necessary to “unlock” this information via map processing. For two study areas in northern and central Jutland, Denmark, we apply object-based image analysis, vector GIS, colour image segmentation, and machine learning processes to produce machine-readable layers for the land use and land cover categories forest, wetland, heath, dune sand, and water bodies from topographic maps from the late nineteenth century. Obtained overall accuracy was 92.3%. A comparison with a contemporary map revealed spatially explicit landscape dynamics dominated by transitions from heath and wetland to agriculture and forest and from heath and dune sand to forest. However, dune sand was also characterised by more complex transitions to heath and dry grassland, which can be related to active prevention of sand drift but that can also be biased by different categorisations of dune sand between the historical and contemporary data. We conclude that automated production of machine-readable layers of land use and land cover categories from historical topographical maps offers a resource-efficient alternative to manual vectorisation and is particularly useful for spatially explicit assessments of long-term landscape dynamics. Our results also underline that an understanding of mapped categories in both historical and contemporary maps is critical to the interpretation of landscape dynamics. Graphical abstract

The Sarough watershed in NW Iran hosts a large amount of mineral occurrences and ore deposits which may be considered as the source of heavy metals in the region. The area has been studied previously; however, the methodology of this paper was less focused on previous studies. This study aimed to assess water quality, determine the spatial distribution pattern, and identify the sources of heavy metals in the main tributaries of Sarough watershed using pollution indexes, multivariate statistical methods, and processing data by geographic information system. Totally, 51 water samples were collected along the main rivers to determine the concentrations of heavy metals by ICP-MS. Regarding the drinking water, agriculture, and freshwater aquatic life guidelines, the rivers were assumed unsafe considering most of toxic elements’ content, especially As. The mean values for heavy metal pollution indexes (HPI: 237.32) and metal indexes (MI: 25.37) indicated the intensive heavy metal pollution. The cluster analysis categorized the 51 sampling sites into four clusters with respect to pollution level. The results obtained from the Kruskal–Wallis and multiple comparison tests had the harmony with the results of CA in introducing the most impacted sampling sites and the parameters responsible for water quality degradation. The results of PCA showed the maximum similarity between As, Sb, Se, Fe, and Mn as well as base metals which was attributed to anthropogenic input from mining and mineral processing wastes. Association of Cr and Ni may suggest a lithology source (weathering of metamorphosed ultramafic outcrops). The maps prepared in the GIS system showed the spatial distribution pattern of toxic elements with maximum values nearby mining sites which decreases gradually toward downstream areas. Finally, the results showed that the Sarough River and its tributaries are influenced by high concentrations of heavy metals from the drainages of mining and ore processing sites and naturally occurring metal loadings as well as the geogenic sources such as weathering of geologic formations and hot springs.

Water resource management is becoming essential due to many anthropogenic and climatic factors resulting in dwindling water resources. Traditionally, geographic information systems (GIS) and remote sensing (RS) have long been instrumental in water resource assessment and management as the satellites or airborne units are periodically utilized to collect data from large areal extent. However, these platforms have limited computational capability and localized storage systems. Recently, these limitations have been overcome by the application of Google Earth Engine (GEE) that offers a faster and more reliable cloud-based GIS and remote sensing platform that leverages its parallel processing capabilities. Thereby, in recent years, GEE has witnessed rapid and accelerated adoption and usage in a wide variety of domains, including water resource monitoring, assessment and management. However, no systematic studies have been made to review the GEE application in water resource management. This review article is a maiden attempt towards developing an understanding of the functioning of GEE and its application in water resource assessment, covering both of its aspects viz (a) water quantity and (b) water quality. The review further attempts to illustrate its capabilities in real-world utility, through a case study conducted to analyze water quality and quantity of lake mead, a reservoir of Hoover Dam, Nevada (USA), at a monthly scale for a 3-year period spanning from 2021 to 2023. The results of this case study showcase the applicability of GEE to the water resource quantity and quality monitoring, assessment and management problems. The review further discusses the existing challenges with the application of GEE in water resource assessment and the scope for further improvement. In conclusion, after tackling the existing challenges with GEE, the application of GEE in water resources has huge potential for management planning of our water resources by addressing the forthcoming challenges. Graphical Abstract