scholarly journals Qualitative Spatial Representation for the Humanities

2019 ◽  
Vol 13 (1-2) ◽  
pp. 2-27 ◽  
Author(s):  
John G. Stell
Keyword(s):  
Geographical Information Systems ◽  
Spatial Representation ◽  
Spatial Reasoning ◽  
Spatial Relations ◽  
Geographical Information ◽  
Computational Techniques ◽  
Computational Technique ◽  
Qualitative Spatial Reasoning ◽  
Gis Technology ◽  
Geographical Space

‘Qualitative spatial reasoning and representation’ is a range of techniques developed in Artificial Intelligence to meet the need for a computational treatment of qualitative spatial relations. Examples of such relations include ‘next to’, ‘overlapping’, ‘to the left of’, ‘separate from’, ‘including’, and so on. These relations occur within the data found in the spatial humanities, but the computational techniques described here do not appear to have been used in connection with this context. While Geographical Information Systems (GIS) are widely used as a means of visualizing and exploring material in the spatial humanities, GIS technology is acknowledged to be ill-suited to information that is vague, uncertain, ambiguous, imprecise or having other qualities that in a scientific setting could be regarded as imperfections. In the humanities such ‘imperfections’ are of course important, and qualitative spatial relations are one source of data that challenges scientifically based GIS. This article reviews the origin of qualitative spatial reasoning and representation in A. N. Whitehead's mereotopology and argues for exploring how these methods could complement GIS as a computational technique in the humanities. Qualitative representation is applicable to modelling spatial arrangements in many domains, not just geographical space. This is demonstrated through an example of spatial relations in lines of printed text.

scholarly journals Geospatial and Social Factors Influencing Morbidity due to Acute Infection in Pediatric Patients in Matiari, Rural Pakistan.

2021 ◽  
Author(s):  
Elise Corden ◽  
Saman Hasan Siddiqui ◽  
Yash Sharma ◽  
Muhammad Faraz Raghib ◽  
William Adorno III ◽  
...  
Keyword(s):  
Random Forest ◽  
Geographical Information Systems ◽  
Respiratory Infections ◽  
Pearson Correlation ◽  
Acute Infection ◽  
Disease Prevalence ◽  
Geographical Information ◽  
Continuous Variables ◽  
Gis Technology ◽  
Healthcare Facilities

Infectious disease is the leading cause of mortality in children under five. This study has investigated environmental factors related to the morbidity of acute respiratory infections (ARIs), diarrhea, and growth using geographical information systems (GIS) technology. Anthropometric, address and disease prevalence data were collected through the SEEM study in Matiari, Pakistan. Publicly available map data was used to compile coordinates of healthcare facilities. A Pearson correlation coefficient (r) was used to calculate the correlation between distance from healthcare facilities and participant growth and morbidity. Other continuous variables influencing these outcomes were analyzed using a random forest regression model. In this study of 416 children, we found participants living closer to secondary hospitals had lower prevalence of ARI (r=0.154, p<0.010) and diarrhea (r=0.228, p<0.001) as well as participants living closer to Maternal Health Centers (MHCs): ARI (r=0.185, p<0.002) and diarrhea (r=0.223, p<0.001) compared to those living near primary facilities. Our random forest model showed distance to have high variable importance in the context of disease prevalence. Our results indicated that participants closer to more basic healthcare facilities reported a higher prevalence of both diarrhea and ARI than those near more urban facilities, highlighting potential public policy gaps in ameliorating rural health.

INTEGRATING SCAT DATA AND GEOGRAPHICAL INFORMATION SYSTEMS TO SUPPORT SHORELINE CLEANUP OPERATIONS

1997 ◽  
Vol 1997 (1) ◽  
pp. 499-506 ◽  
Author(s):  
Alain Lamarche ◽  
Edward H. Owens
Keyword(s):  
Decision Making ◽  
Information System ◽  
Data Analysis ◽  
Data Collection ◽  
Software Package ◽  
Geographical Information Systems ◽  
Data Entry ◽  
Decision Makers ◽  
Geographical Information ◽  
Gis Technology

ABSTRACT An analysis of the work performed by the various teams involved in shoreline cleanup operations has been applied to the design of an approach for the integration of data collected by the SCAT process with electronic maps produced by geographical information system (GIS) technology. This has led to the implementation of a PC-based system that incorporates a database of SCAT information, a knowledge base on oil behavior and shoreline cleanup, and a GIS. The system provides support to data collection using the SCAT approach for field teams and to map-based data analysis for planners and managers. In the course of this work, a set of the maps that are considered the most useful for summarizing information about shoreline conditions was designed and evaluated. This evaluation initially involved consultation with individuals experienced in shoreline cleanup. The applicability of the map representation for decision making was further tested during spill drills. SCAT surveys generate a large volume of data that need to be captured and integrated. There is a risk that this large amount of information might overwhelm decision makers involved in the management of shoreline cleanup operations. The paper describes the various modifications that were made to the SHORECLEAN software package to provide some solutions to these problems. These include providing specialized SCAT data entry forms, automating the links between a SCAT database and a GIS, and producing map representations that provide clear, useful, and nonmisleading information for decision makers.

scholarly journals Using GIS for management of conflicts in natural protected area in Cindrel mountains

2021 ◽  
Vol 343 ◽  
pp. 09011
Author(s):  
Iulian Alexandru Bratu ◽  
Lucian Dincă
Keyword(s):  
Geographical Information Systems ◽  
Natura 2000 ◽  
Management Plan ◽  
Geographical Information ◽  
Site Management ◽  
Gis Technology ◽  
Old Growth Forest ◽  
Legal Nature ◽  
Protected Natural Areas ◽  
Geo Database

This study reflects the possibility of using the GIS technology for the management and resolution of conflicts between stakeholders in the management of protected natural areas that cover large surfaces, such as Natura 2000 sites. The research is accomplished in Frumoasa site from Cindrel Mountains, where a conflict of a legal nature was analysed, in the extinguishment of which the technology of geographical information systems was used. In this sense, the presence of the species and habitats that are found on the surface of the incriminated forest was analysed and the comparison with the list of species and habitats that was the basis for declaring the surface as a nature 2000 site. In the next stage, both the site management plan and the forest management plan were analysed in order to identify inconsistent potentials. Then, maps of the presence and distribution of species and habitats were made, with the protection and conservation measures adopted. Also, special attention was paid to the identification of primary and old-growth forest, their distribution and measures for their conservation. The conclusions include improvements can be made to the management of the incriminated areas, accompanied by the geo-database.

scholarly journals Distance from Healthcare Facilities Is Associated with Increased Morbidity of Acute Infection in Pediatric Patients in Matiari, Pakistan

2021 ◽  
Vol 18 (21) ◽  
pp. 11691
Author(s):  
Elise Corden ◽  
Saman Hasan Siddiqui ◽  
Yash Sharma ◽  
Muhammad Faraz Raghib ◽  
William Adorno ◽  
...  
Keyword(s):  
Random Forest ◽  
Environmental Factors ◽  
Geographical Information Systems ◽  
Pearson Correlation ◽  
Acute Infection ◽  
Disease Prevalence ◽  
Geographical Information ◽  
Continuous Variables ◽  
Gis Technology ◽  
Healthcare Facilities

The relationship between environmental factors and child health is not well understood in rural Pakistan. This study characterized the environmental factors related to the morbidity of acute respiratory infections (ARIs), diarrhea, and growth using geographical information systems (GIS) technology. Anthropometric, address and disease prevalence data were collected through the SEEM (Study of Environmental Enteropathy and Malnutrition) study in Matiari, Pakistan. Publicly available map data were used to compile coordinates of healthcare facilities. A Pearson correlation coefficient (r) was used to calculate the correlation between distance from healthcare facilities and participant growth and morbidity. Other continuous variables influencing these outcomes were analyzed using a random forest regression model. In this study of 416 children, we found that participants living closer to secondary hospitals had a lower prevalence of ARI (r = 0.154, p < 0.010) and diarrhea (r = 0.228, p < 0.001) as well as participants living closer to Maternal Health Centers (MHCs): ARI (r = 0.185, p < 0.002) and diarrhea (r = 0.223, p < 0.001) compared to those living near primary facilities. Our random forest model showed that distance has high variable importance in the context of disease prevalence. Our results indicated that participants closer to more basic healthcare facilities reported a higher prevalence of both diarrhea and ARI than those near more urban facilities, highlighting potential public policy gaps in ameliorating rural health.

Spatial Reasoning and GIS in Linguistic Prehistory. Two Case Studies from Lower Fungom (Northwest Cameroon)

2012 ◽  
Vol 2 (2) ◽  
pp. 150-183 ◽  
Author(s):  
Pierpaolo Di Carlo ◽  
Giovanna Pizziolo
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Real World ◽  
Interdisciplinary Research ◽  
Historical Development ◽  
Language Change ◽  
Spatial Reasoning ◽  
Spatial Relations ◽  
Geographical Information ◽  
Ongoing Project

Being an ontologically multidisciplinary topic, language change is among the best candidates to be addressed using Geographic Information Systems (GIS). GIS can integrate datasets from diverse disciplines along with real-world geographical information, hence facilitating the investigation of (i) the spatial relations existing between research items and (ii) (past) landscapes. Drawing from an ongoing project focused on the historical development of the extremely diverse linguistic situation documented in the Lower Fungom region (Northwest Cameroon), this article explores the possibility of placing authentic interdisciplinary research pivoting on linguistic issues within a GIS framework.

scholarly journals Combining Deep Learning and Qualitative Spatial Reasoning to Learn Complex Structures from Sparse Examples with Noise

2019 ◽  
Vol 33 ◽  
pp. 2911-2918 ◽  
Author(s):  
Nikhil Krishnaswamy ◽  
Scott Friedman ◽  
James Pustejovsky
Keyword(s):  
Deep Learning ◽  
Heuristic Search ◽  
Spatial Reasoning ◽  
Spatial Relations ◽  
Search Space ◽  
Training Data ◽  
Learning Approaches ◽  
Qualitative Spatial Reasoning ◽  
Heuristic Search Algorithms ◽  
Novel Approach

Many modern machine learning approaches require vast amounts of training data to learn new concepts; conversely, human learning often requires few examples—sometimes only one—from which the learner can abstract structural concepts. We present a novel approach to introducing new spatial structures to an AI agent, combining deep learning over qualitative spatial relations with various heuristic search algorithms. The agent extracts spatial relations from a sparse set of noisy examples of block-based structures, and trains convolutional and sequential models of those relation sets. To create novel examples of similar structures, the agent begins placing blocks on a virtual table, uses a CNN to predict the most similar complete example structure after each placement, an LSTM to predict the most likely set of remaining moves needed to complete it, and recommends one using heuristic search. We verify that the agent learned the concept by observing its virtual block-building activities, wherein it ranks each potential subsequent action toward building its learned concept. We empirically assess this approach with human participants’ ratings of the block structures. Initial results and qualitative evaluations of structures generated by the trained agent show where it has generalized concepts from the training data, which heuristics perform best within the search space, and how we might improve learning and execution.

Including the spatial dimension: using geographical information systems in hydrology

1996 ◽  
Vol 20 (2) ◽  
pp. 159-177 ◽  
Author(s):  
R.A. McDonnell
Keyword(s):  
Information Systems ◽  
Spatial Data ◽  
Geographical Information Systems ◽  
Spatial Dimension ◽  
Geographical Information ◽  
Gis Technology ◽  
Gis Data ◽  
Spatial Data Models ◽  
The Many ◽  
Hydrological Applications

Developments in geographical information systems (GIS) technology have coincided with moves within hydrology to a more explicit accounting of space through distributed rather than lumped or topological representations. GIS support these spatial data models and provide integrating, measuring and analytical capabilities which have been used in many hydrological applications ranging from inventory and assessment studies through to process modelling. The many examples in the article illustrate how the technology has supported moves away from averaged value representations for catchments towards a greater inclusion of spatial variations in hydrological studies. While the potential of these systems is gradually being realized, there are still various issues, both technical and methodological, which at present limit their use. As new data sources become available, GIS data structures become more flexible and open, and, as the understanding of scale variations in processes improves, the possibilities for using the technology in hydrological research will expand.

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