Gaurav Sinha

Cognition-Based Extraction and Modelling of Topographic Eminences

Abstract Terrain is generally stored in GIS as an elevation field, whereas human cognition of the landscape is usually object based. To address this mismatch of terrain data models, we propose object-based terrain representation, using topographic eminences, which are landforms that rise up conspicuously from the ground to visibly dominate the landscape, to illustrate our case. We propose a cognition-based methodology for automated detection and delineation of eminences from digital elevation models (DEMs). Alternative conceptualizations of the landscape can be realized by simple manipulation of intuitive parameters such as a peaks relative height and distance. Our approach delimits the extent of eminences based purely on topographic gradient and aspect, much like the delineation of ridges as watershed boundaries. Smaller eminences can be incrementally aggregated into larger cognitive wholes, enabling scale-sensitive landscape reconstruction. The ability to integrate field and object views of the landsca...

A visual basic program for ridge axis picking on DEM data using the profile-recognition and polygon-breaking algorithm

For many scientists working with digital topographic data, extracting lineaments or linear features is an important step in structuring and analyzing raw data. A ridge axis, which represents the top a mountain ridge, is one of the most important topographic features used in a wide variety of applications. Algorithms and software for automating the extraction of ridges or ridge axes from DEMs are, however, still not easily available or not widely acceptable. In this paper, we present a user-friendly Visual Basic program that automates the extraction of the ridge axis system from DEM data, based on the profile-recognition and polygon-breaking algorithm (PPA). An important feature of PPA is that it takes a global approach, as opposed to the local neighborhood operators used in many other algorithms. Each segment detected by PPA considers not only relations with contiguous neighboring grid points, but also strives to preserve the continuity of the global trend. This is an attempt to simulate human operators, who always factor in the overall trend of the lineament before delineating its local parts. PPA starts by connecting all points in a neighborhood that can possibly lie on the ridge axis, thus forming a belt of polygons in the first step. Next, a polygon breaking process eliminates unwanted segments according to the assumption that a ridge segment cannot be the side of any closed polygon, and that the result should be a purely dendritic line pattern. Finally, a branch-reduction process is executed to eliminate all parallel false ridges that remained due to the conservative approach taken in the first step. Results indicate that PPA is reasonably successful in picking out ridges that would have been identified manually by experts. In addition to providing a detailed user interface for executing PPA, several modifications were made to significantly improve the computational efficiency of PPA, as compared to the original version published in 1998. The source codes are provided for free download on the website listed above.

An ontology design pattern for surface water features

Surface water is a primary concept of human experience but concepts are captured in cultures and languages in many different ways. Still, many commonalities exist due to the physical basis of many of the properties and categories. An abstract ontology of surface water features based only on those physical properties of landscape features has the best potential for serving as a foundational domain ontology for other more context-dependent ontologies. The Surface Water ontology design pattern was developed both for domain knowledge distillation and to serve as a conceptual building-block for more complex or specialized surface water ontologies. A fundamental distinction is made in this ontology between landscape features that act as containers (e.g., stream channels, basins) and the bodies of water (e.g., rivers, lakes) that occupy those containers. Concave (container) landforms semantics are specified in a Dry module and the semantics of contained bodies of water in a Wet module. The pattern is implemented in OWL, but Description Logic axioms and a detailed explanation is provided in this paper. The OWL ontology will be an important contribution to Semantic Web vocabulary for annotating surface water feature datasets. Also provided is a discussion of why there is a need to complement the pattern with other ontologies, especially the previously developed Surface Network pattern. Finally, the practical value of the pattern in semantic querying of surface water datasets is illustrated through an annotated geospatial dataset and sample queries using the classes of the Surface Water pattern.

Use of geostatistics for remediation planning to transcend urban political boundaries

Soil remediation plans are often dictated by areas of jurisdiction or property lines instead of scientific information. This study exemplifies how geostatistically interpolated surfaces can substantially improve remediation planning. Ordinary kriging, ordinary co-kriging, and inverse distance weighting spatial interpolation methods were compared for analyzing surface and sub-surface soil sample data originally collected by the US EPA and researchers at the University at Buffalo in Hickory Woods, an industrial-residential neighborhood in Buffalo, NY, where both lead and arsenic contamination is present. Past clean-up efforts estimated contamination levels from point samples, but parcel and agency jurisdiction boundaries were used to define remediation sites, rather than geostatistical models estimating the spatial behavior of the contaminants in the soil. Residents were understandably dissatisfied with the arbitrariness of the remediation plan. In this study we show how geostatistical mapping and participatory assessment can make soil remediation scientifically defensible, socially acceptable, and economically feasible.

The Pedagogical Benefits of Participatory GIS for Geographic Education

ABSTRACT In this article, participatory GIS (PGIS) is explored and established as a powerful platform for geographic education. PGIS pedagogy can help educators meet diverse learning objectives pertaining to: (1) local knowledge and place-based thinking; (2) community engagement; (3) field mapping with geospatial technologies; (4) mixed-methods research; and (5) fostering of critical reflexivity in students. The discussion is supported with insights from multiple student-facilitated PGIS projects organized in rural Tanzania. There also is a thorough discussion of the challenges and caveats associated with involving students in PGIS projects, and a call for new research for assessing and advancing PGIS pedagogy.

Multicriteria generalization MCG: a decision-making framework for formalizing multiscale environmental data reduction

Environmental simulation models need automated geographic data reduction methods to optimize the use of high-resolution data in complex environmental models. Advanced map generalization methods have been developed for multiscale geographic data representation. In the case of map generalization, positional, geometric and topological constraints are focused on to improve map legibility and communication of geographic semantics. In the context of environmental modelling, in addition to the spatial criteria, domain criteria and constraints also need to be considered. Currently, due to the absence of domain-specific generalization methods, modellers resort to ad hoc methods of manual digitization or use cartographic methods available in off-the-shelf software. Such manual methods are not feasible solutions when large data sets are to be processed, thus limiting modellers to the single-scale representations. Automated map generalization methods can rarely be used with confidence because simplified data sets may violate domain semantics and may also result in suboptimal model performance. For best modelling results, it is necessary to prioritize domain criteria and constraints during data generalization. Modellers should also be able to automate the generalization techniques and explore the trade-off between model efficiency and model simulation quality for alternative versions of input geographic data at different geographic scales. Based on our long-term research with experts in the analytic element method of groundwater modelling, we developed the multicriteria generalization (MCG) framework as a constraint-based approach to automated geographic data reduction. The MCG framework is based on the spatial multicriteria decision-making paradigm since multiscale data modelling is too complex to be fully automated and should be driven by modellers at each stage. Apart from a detailed discussion of the theoretical aspects of the MCG framework, we discuss two groundwater data modelling experiments that demonstrate how MCG is not just a framework for automated data reduction, but an approach for systematically exploring model performance at multiple geographic scales. Experimental results clearly indicate the benefits of MCG-based data reduction and encourage us to continue expanding the scope of and implement MCG for multiple application domains.

Using Video Direct Observation to Assess Children’s Physical Activity During Recess

PURPOSE Traditional direct observation cannot provide continuous, individual-level physical activity (PA) data throughout recess. This study piloted video direct observation to characterize childrens recess PA overall and by sex and weight status. METHODS Children (N = 23; 11 boys; 6 overweight; third to fifth grade) were recorded during 2 recess periods, coding for PA duration, intensity, location, and type. Duration of PA type and intensity across sex and weight status overall and between/within locations were assessed using 1- and 2-way analysis of variances. RESULTS The field elicited more sedentary behavior (39% of time) and light PA (17%) and less moderate to vigorous PA (41%) compared with the fixed equipment (13%, 7%, and 71%, respectively) or the court (21%, 7%, and 68%, respectively). Boys engaged in significantly more vigorous-intensity activity on the court (35%) than girls (14%), whereas girls engaged in more moderate to vigorous PA on the fixed equipment (77% vs 61%) and field (46% vs 35%) than boys (all Ps > .05). PA type also differed by sex and weight status. CONCLUSION Video direct observation was capable of detecting and characterizing childrens entire recess PA while providing valuable context to the behavior. The authors confirmed previous findings that PA intensity was not uniform by schoolyard location and further differences exist by sex and weight status.

A Reference Landform Ontology for Automated Delineation of Depression Landforms from DEMs

This reference landform ontology is intended to guide automated delineation of landforms from digital elevation models (DEMs) and semantic information retrieval about landforms. Since only form related information is available from DEMs, the categories of this reference ontology are defined based only on morphological criteria. The choice of the landform categories is informed by ethnophysiographic and spatial cognition research. The proposed taxonomy is work in progress and reflects the current focus on automated delineation and mapping of depression landforms (e.g., basins, valleys and canyons).

Validating GEOBIA Based Terrain Segmentation and Classification for Automated Delineation of Cognitively Salient Landforms

Landform objects extracted from Geographic Object Based Image Analysis (GEOBIA) based terrain segmentation to locations are overlaid and compared to feature types of landforms mapped in the USGS maintained Geographic Names Information System (GNIS) topographic database. GEOBIA terrain objects were found to statistically related to GNIS feature classes. Comparison of GNIS feature classes and GEOBIA landform classes suggests that GEOBIA landform class semantics correspond well with naive geographic conceptualizations reflected in GNIS feature types.

Media Deserts: Monitoring the Changing Media Ecosystem

This chapter describes the Media Deserts Project, which allows researchers to monitor the health of media ecosystems and provides a valuable tool in policy and resource allocation. By mapping circulation data onto geographies, the Media Deserts Project relies on a geographic framework and a geographic information system technology to assess and track the changes in the information health of communities across the USA. The team behind the project is working to create new mapping tools for researchers, policymakers, and local leaders to help them identify communities lacking access to local news and information, as well as measure trends in access to critical news and information needs and where valuable human and capital resources might be deployed to establish or restore news and information coverage.