Today, various methods are applied to analyze the data collected through participatory mapping, including public participation GIS (PPGIS), participatory GIS (PGIS), and collecting volunteered ...geographic information (VGI). However, these methods lack an organized framework to describe and guide their systematic applications. Majority of the published articles on participatory mapping apply a specific subset of analyses that fails to situate the methods within a broader, more holistic context of research and practice. Based on the expert workshops and a literature review, we synthesized the existing analysis methods applied to the data collected through participatory mapping approaches. In this article, we present a framework of methods categorized into three phases: Explore, Explain, and Predict/Model. Identified analysis methods have been highlighted with empirical examples. The article particularly focuses on the increasing applications of online PPGIS and web-based mapping surveys for data collection. We aim to guide both novice and experienced practitioners in the field of participatory mapping. In addition to providing a holistic framework for understanding data analysis possibilities, we also discuss potential directions for future developments in analysis of participatory mapping data.
The aim of this study is evaluate water quality of the Aksu River, the main river recharging the Karacaören-1 Dam Lake and flowing approximately 145km from Isparta province to Mediterranean. Due to ...plan for obtaining drinking water from the Karacaören-1 Dam Lake for Antalya Province, this study has great importance. In this study, physical and chemical analyses of water samples taken from 21 locations (in October 2011 and May 2012, two periods) through flow path of the river were investigated. The analysis results were compared with maximum permissible limit values recommended by World Health Organization and Turkish drinking water standards. The water quality for drinking purpose was evaluated using the water quality index (WQI) method. The computed WQI values are between 35.6133 and 337.5198 in the study. The prepared WQI map shows that Karacaören-1 Dam Lake generally has good water quality. However, water quality is poor and very poor in the north and south of the river basin. The effects of punctual and diffuse pollutants dominate the water quality in these regions. Furthermore, the most effective water quality parameters are COD and Mg on the determination of WQI for the present study.
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•This study aims to evaluate water quality of the Aksu River.•The water quality for drinking purpose was evaluated using water quality index (WQI) method.•The effect of pollutants is dominant on to water quality in the region.•COD and Mg are the most effective water quality parameters.
Real-space time evolution of an electron wave packet through single- and multi-layer graphene is numerically calculated with taking the effect of inelastic scattering into account for the first step ...of numerical simulation of graphene-insulator-semiconductor (GIS)-structured electron source. The incident electron energy is ∼10 eV supposing the typical experimental condition. Wave packet inevitably introduces an energy uncertainty, which is rather preferable for the simulation of real electron source with energy spread. The graphene potential for the incident electron is calculated from the electron density obtained by the density functional method and the screened Coulomb potential. The inelastic scattering effect is included in the calculation by assuming the imaginary potential proportional to the real graphene potential with a proportionality constant a. Comparing the obtained simulation results with the experimental ones, the inelastic parameter is determined as a ≃ 0.03. Simulation of realistic three-layer GIS structure remains as a future work, where the effects of inelastic scattering should be included by the above method with a ≃ 0.03.
Government organizations in the developing world have been at the forefront of ongoing reforms that have prompted their use of GIS and other information and communication technologies for urban ...governance. However, there have been very few examinations of GIS spatial knowledge construction in a non-Western context. Particularly, very little is known about the growing and varying use of GIS and spatial information by urban local bodies in India in the midst of India's changing urban governance culture. This article presents an in-depth examination of Surat Municipal Corporation (SMC), one of India's leading urban local bodies as the corporation implements e-governance strategies including the use of GIS and spatial information. Drawing from Critical GIS literature and GIS implementation and diffusion literature, this article uses an integrated approach to examine SMC's GIS spatial knowledge construction. The article demonstrates that in the case of SMC, GIS knowledge construction is not only shaped by SMC's proactive role in positioning itself with the national government's priorities and agendas, but also by the presence of powerful actors who play an instrumental role in introducing change and innovation. This article is part of a larger project that aims to investigate the process of GIS spatial knowledge construction situated in contemporary India.
With the increase in smart devices, spatiotemporal data has grown exponentially. To deal with challenges caused by an increase data requires a scalable and efficient architecture that can store, ...query, analyze, and visualize spatiotemporal big data. This paper describes a Cloud-terminal integrated GIS platform architecture designed to meet the requirements of processing and analyzing spatiotemporal big data. Cloud-terminal Integration GIS is developed according to the architecture. Extensive experiments deployed on the internal organization cluster using real-time datasets showed that the SuperMap GIS spatiotemporal big data engine achieved excellent performance.
•We propose an integrated GIS platform architecture designed to meet the requirements of processing and analyzing spatiotemporal big data.•Cloud-terminal Integration GIS for spatiotemporal big data is developed.•The experiments for spatiotemporal big data showed SuperMap GIS spatiotemporal big data engine achieved excellent performance.
•This paper revisited the research progress in the field of spatial optimization, covering its characteristics, modeling approaches, solving methods, and application areas.•The development of ...geospatial big data and GeoAI offers new opportunity to tackle complex spatial optimization problems.•The explosive growth of geospatial big data poses challenges for spatial optimization.•The interpretability and transferability of GeoAI, as well as its integration with spatial optimization, remain challenges.
Spatial optimization is an interdisciplinary field dedicated to the scientific and rational allocation of resources spatially, which has received tremendous attention across various disciplines including geography, operations research, management science, and computer science. Spatial optimization provides important theoretical foundations and solutions for determining optimal spatial arrangements or configurations of entities, resources, or goods. However, the complexity of spatial optimization problems poses critical challenges in spatial optimization problems modeling, and efficiently solving. Recently, the surge of multi-source geospatial big data, the emerging technologies such as geospatial artificial intelligence (GeoAI), and the advancements of computing technologies along with the ever-expanding capabilities of computer and data storage resources, have created significant opportunities to the effective and efficient addressing of spatial optimization issues, even though numerous challenges still exist. Therefore, this paper aims to revisit the existing literature of spatial optimization quantitatively and qualitatively, as well as reflect on the opportunities and challenges, especially posed by geospatial big data and GeoAI. Through these efforts, we seek to stimulate greater engagement in spatial optimization research and practices, accelerate the integration of novel technologies and methods, as well as collectively advance the development of the field.
With the advancements of geospatial technologies, geospatial datasets of fine particulate matter (PM
) and mortality statistics are increasingly used to examine the health effects of PM
. Choices of ...these datasets with difference geographic characteristics (e.g., accuracy, scales, and variations) in disease burden studies can significantly impact the results. The objective of this study is to revisit the estimations of PM
-attributable mortality by taking advantage of recent advancements in high resolution mapping of PM
concentrations and fine scale of mortality statistics and to explore the impacts of new data sources, geographic scales, and spatial variations of input datasets on mortality estimations. We estimate the PM
-mortality for the years of 2000, 2005, 2010 and 2015 using three PM
concentration datasets Chemical Transport Model (CTM), random forests-based regression kriging (RFRK), and geographically weighted regression (GWR) at two resolutions (i.e., 10 km and 1 km) and mortality rates at two geographic scales (i.e., regional-level and county-level). The results show that the estimated PM
-mortality from the 10 km CTM-derived PM
dataset tend to be smaller than the estimations from the 1 km RFRK- and GWR-derived PM
datasets. The estimated PM
-mortalities from regional-level mortality rates are similar to the estimations from those at county level, while large deviations exist when zoomed into small geographic regions (e.g., county). In a scenario analysis to explore the possible benefits of PM
concentrations reduction, the uses of the two newly developed 1 km resolution PM
datasets (RFRK and GWR) lead to discrepant results. Furthermore, we found that the change in PM
concentration is the primary factor that leads to the PM
-attributable mortality decrease from 2000 to 2015. The above results highlight the impact of the adoption of input datasets from new sources with varied geographic characteristics on the PM
-attributable mortality estimations and demonstrate the necessity to account for these impact in future disease burden studies. CAPSULE: We revisited the estimations of PM
-attributable mortality with advancements in PM
mapping and mortality statistics, and demonstrated the impact of geographic characteristics of geospatial datasets on mortality estimations.
This paper takes stock of geographical contributions to the study of energy and energy futures. The paper is written in two parts. First, I trace the methodological and philosophical traditions that ...underpin geographical approaches to energy studies. I argue that while ‘energy geography’ is arguably a pragmatic shorthand with which to communicate to the broader energy studies community, geographical studies of energy have expanded in scope and theoretical plurality so that ‘energy geographies’ is a more appropriate label. Energy geographers are well positioned to contribute to scientific and policy debates surrounding energy due to their privileged position at the borderland between various philosophical and methodological traditions. Second, I identify some of the problems, opportunities and uncertainties that contemporary energy geographers are helping to identify, understand, and resolve. Past contributions and critical issues for future scholarship are highlighted in four themes: (1) using advanced socio-spatial theory to better understand the energy-society relationship; (2) geo-political and geo-economic assessments of (changing) global energy trade networks; (3) geographical perspectives on socio-technical (energy) transitions; and (4) advanced spatial decision-support for energy planning and technology implementation. While this discussion is by no means exhaustive, it aims to bring some clarity and specificity to the policy and academic relevance of geographical thought and practice as it relates to energy issues.
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•A new “information system” is proposed for mineral exploration targeting.•The system is called “exploration information system” (EIS).•EIS uses “mineral system” concepts in ...conjunction with GIS.•Ore-forming processes are classified into pre-, syn-, and post-mineralization parts.•EIS aims to automated translation of ore-forming processes into spatial proxies.
The advent of modern data collection and storage technologies has brought about a huge increase in data volumes with both traditional and machine learning tools struggling to effectively handle, manage and analyse the very large data quantities that are now available. The mineral exploration industry is by no means immune to this big data issue. Exploration decision-making has become much more complex in the wake of big data, in particular with respect to questions about how to best manage and use the data to obtain information, generate knowledge and gain insight. One of the ways in which the mineral exploration industry works with big data is by using a geographic information system (GIS). For example, GIS platforms are often used for integration, interrogation and interpretation of diverse geoscience and mineral exploration data with the goal of refining and prioritising known and identifying new targets. Here we (i) briefly discuss the importance of carefully translating conceptual ore deposit models into effective exploration targeting maps, (ii) propose and describe what we term exploration information systems (EIS): a new idea for an information system designed to better integrate the conceptual mineral deposit model (i.e., the critical and constituent processes of the targeted mineral system) with data available to support exploration targeting, and (iii) discuss how best to categorise mineral systems in an EIS as scale-dependent subsystems to form mineral deposits. Our vision for the future use of EIS in exploration targeting is one whereby the mappable ingredients of a targeted mineral system are translated and combined into a set of weighted evidence (or proxy) maps automatically, resulting in an auto-generated mineral prospectivity map and a series of ranked exploration targets. We do not envisage the EIS replacing human input and ingenuity; rather we envisage the EIS as an additional tool in the exploration toolbox and as an intelligence amplifying system in which humans are making use of machines to achieve the best possible results.
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