Steven D. Prager

Assessment and Evaluation of GIScience Curriculum using the Geographic Information Science and Technology Body of Knowledge

Academic institutions are increasingly being held accountable for the quality of education which is, in turn, leading to an increased emphasis on curriculum assessment. This is especially true of geographic information science & technology (GIS&T), in which a rapidly growing profession demands that educational programs produce highly qualified graduates. In response to these demands, the University Consortium of Geographic Information Science (UCGIS) has developed the Geographic Information Science and Technology Body of Knowledge, to identify the broad spectrum of knowledge, skills and techniques that make up the GIS&T domain. An intended use of this document is to support the development and assessment of GIS&T curricula. The authors address the potential benefits of using the Body of Knowledge through an evaluation of the learning objectives and curriculum of sample courses at two universities. They find that the Body of Knowledge enables robust specification of objectives and curricula, and provides the platform for reproducible and consistent evaluation of both curriculum and, ultimately, student outcomes. It is also flexible in allowing programmes to evaluate curricula based on their own goals and missions, rather than against a single standard curriculum.

Foundations of sustainability information representation theory: spatial–temporal dynamics of sustainable systems

A critical need exists to broaden and deepen sustainability information foundations that can foster growth of actionable knowledge about human–environment relations to address grand challenges in sustainable system domains such as sustainable development, social–ecological systems, and hazards influencing global environmental change. Broad-based information is needed to integrate across domains to address sustainability problems cast as complex systems problems that vary widely across space and time. Deep-based information is needed to address nuanced and contextual relationships that exist within and across domains. Both broad and deep information together are needed to better address spatial–temporal dynamics in complex sustainable systems. According to many publications, the concept of sustainable systems is considered to be at the core of self-organizing systems; and in turn, the concept of self-organizing systems is at the core of social–ecological systems, coupled natural–human systems, and human–environment systems. The sustainable systems concept is elucidated in terms of ontological and epistemological foundations from geographic information representation theory. A framework for Measurement-informed Ontology and Epistemology for Sustainability Information Representation, drawing from research about ontology and epistemology in geographic information science, provides the foundation for elucidating concepts and relations about sustainability information representation in general and sustainable systems in particular. The framework is developed to form a core of sustainability information representation theory, and consequently provides a basis for articulating first principles about the character of space–time data models that can be used to create computational models within geographic information systems (GIS). An example applies the framework to common pool resources as sustainable systems. Developing the framework and exploring an example fosters intellectual bridge building between sustainability science and sustainability management in the form of a sustainability information science. That intellectual bridge building of sustainability information science supports societal progress moving knowledge into decision action toward sustainable development, encouraging new insight for designs of space–time data models, and extending GIS as an information technology foundation for sustainability management. Conclusions and directions for next steps in research about sustainability information representation theory in general and sustainable systems in particular are offered.

Environmental contextualization of uncertainty for moving objects

Abstract Spatiotemporal objects are frequently characterized not only in terms of their position, but also by the uncertainty associated with the measurement of that position. Uncertainty metrics are typically parametric and serve in support of the estimation of probable location for a given object. Unfortunately, this parametric approach fails to model the manner in which the environment influences the movement of a spatiotemporal object and, commensurately, the effect of the environment on uncertainty. This research systematically considers the effect of environmental objects and fields in terms of their influence on movement of spatiotemporal objects. Utilizing a simple ontology of spatiotemporal uncertainty, the manner in which the semantic characteristics of environmental objects serve to contextualize an otherwise purely parametric estimation of spatiotemporal uncertainty is examined. The ontology allows for reallocation of positional probability through the environmental contextualization of each probable point of occurrence and spatial reallocation of the contextualized points via an approach wherein “hindrances” such as buildings serve to repel probability of occurrence, and “enablers” such as roads serve to attract probable occurrences. The demonstrated characterization of uncertainty offers opportunities for increased efficiency with respect to spatiotemporal database queries and the possibility for increased accuracy with regard to the representation of probable historic, current and future trajectories.

From Observation to Information: Data-Driven Understanding of on Farm Yield Variation

Agriculture research uses “recommendation domains” to develop and transfer crop management practices adapted to specific contexts. The scale of recommendation domains is large when compared to individual production sites and often encompasses less environmental variation than farmers manage. Farmers constantly observe crop response to management practices at a field scale. These observations are of little use for other farms if the site and the weather are not described. The value of information obtained from farmers’ experiences and controlled experiments is enhanced when the circumstances under which it was generated are characterized within the conceptual framework of a recommendation domain, this latter defined by Non-Controllable Factors (NCFs). Controllable Factors (CFs) refer to those which farmers manage. Using a combination of expert guidance and a multi-stage analytic process, we evaluated the interplay of CFs and NCFs on plantain productivity in farmers’ fields. Data were obtained from multiple sources, including farmers. Experts identified candidate variables likely to influence yields. The influence of the candidate variables on yields was tested through conditional forests analysis. Factor analysis then clustered harvests produced under similar NCFs, into Homologous Events (HEs). The relationship between NCFs, CFs and productivity in intercropped plantain were analyzed with mixed models. Inclusion of HEs increased the explanatory power of models. Low median yields in monocropping coupled with the occasional high yields within most HEs indicated that most of these farmers were not using practices that exploited the yield potential of those HEs. Varieties grown by farmers were associated with particular HEs. This indicates that farmers do adapt their management to the particular conditions of their HEs. Our observations confirm that the definition of HEs as recommendation domains at a small-scale is valid, and that the effectiveness of distinct management practices for specific micro-recommendation domains can be identified with the methodologies developed.

Utilization of spatial decision support systems decision-making in dryland agriculture

FSAW delineated Wyoming agricultural land into relative ranks for burclover establishment.Defuzzification produced final output map with crisp scores and calculated centroid.Calculated centroid map demonstrated efficacy of SDSS in agricultural decision-making.Effective land suitability ranking validated value of ex-ante agricultural technologies.Presented information has potential to determine burclover feasibility in Wyoming. Integrated Geographic Information Systems (GIS) and spatial decision support systems (SDSS) methods are important for relative ranking of suitability of agricultural land. This case study was conducted at the University of Wyoming in 2007 to demonstrate viability of integrated GIS and SDSS methods as useful ex-ante assessment technologies to help rank relative suitability of Wyoming agricultural land for optimum establishment of Laramie Tifton burclover Medicago rigidula (L.) Allioni in the Central High Plains agricultural region. The study uses fuzzy set logic methods and implements the fuzzy simple additive weighting (FSAW) method through modeling in GIS raster to analyze Wyoming States agricultural land use, and the identified suitability attributes for optimum burclover establishment; the long-term summer diurnal temperature flux, September-October precipitation, and April-July precipitation. Further, the study uses one of the two categories of multiple criteria decision-making (MCDM) known as multiple attribute decision making (MADM), to combine the range of each attributes possible suitability values in meaningful ways that allow suitability criteria to be evaluated on the basis of low, medium, and high suitability for optimum burclover establishment. The inverse distance weighting (IDW) interpolation technique interpolated the point shape files of the identified suitability attributes and produced surface maps that allowed characterization of long-term summer diurnal temperature flux and seasonal precipitation for the State of Wyoming. The fuzzy additive weighting and defuzzification methods transformed data from different sources into useful information that can be effectively used to enhance decision making in agriculture. Finally, defuzzification transformed fuzzy scores into useful crisp scores and produced the final output map with calculated centroid. The resulting calculated trapezoidal centroid map with useful crisp scores from transformed disparate fuzzy data demonstrates that spatial suitability analysis can be used effectively to enhance decision making in agricultural planning and management. Likewise, the effective ranking of relative suitability of Wyoming agricultural land for optimum establishment of Laramie Tifton burclover validates the value of using fuzzy set logic and additive weighting approaches for ex-ante assessment of the potential suitability of agricultural technologies.

Assessing weather-yield relationships in rice at local scale using data mining approaches

Seasonal and inter-annual climate variability have become important issues for farmers, and climate change has been shown to increase them. Simultaneously farmers and agricultural organizations are increasingly collecting observational data about in situ crop performance. Agriculture thus needs new tools to cope with changing environmental conditions and to take advantage of these data. Data mining techniques make it possible to extract embedded knowledge associated with farmer experiences from these large observational datasets in order to identify best practices for adapting to climate variability. We introduce new approaches through a case study on irrigated and rainfed rice in Colombia. Preexisting observational datasets of commercial harvest records were combined with in situ daily weather series. Using Conditional Inference Forest and clustering techniques, we assessed the relationships between climatic factors and crop yield variability at the local scale for specific cultivars and growth stages. The analysis showed clear relationships in the various location-cultivar combinations, with climatic factors explaining 6 to 46% of spatiotemporal variability in yield, and with crop responses to weather being non-linear and cultivar-specific. Climatic factors affected cultivars differently during each stage of development. For instance, one cultivar was affected by high nighttime temperatures in the reproductive stage but responded positively to accumulated solar radiation during the ripening stage. Another was affected by high nighttime temperatures during both the vegetative and reproductive stages. Clustering of the weather patterns corresponding to individual cropping events revealed different groups of weather patterns for irrigated and rainfed systems with contrasting yield levels. Best-suited cultivars were identified for some weather patterns, making weather-site-specific recommendations possible. This study illustrates the potential of data mining for adding value to existing observational data in agriculture by allowing embedded knowledge to be quickly leveraged. It generates site-specific information on cultivar response to climatic factors and supports on-farm management decisions for adaptation to climate variability.

Disaggregating human population for improved land use management in Kenya

Understanding the spatial distribution of population across a landscape is important in land use planning. In developing nations, where resources are limited, such information can facilitate more efficient decision-making for resource allocations. This article examines three methods for characterizing the distribution of human population within a natural boundary that overlaps but is substantially different from the geography of the corresponding census data. Using land cover information in conjunction with available census data, simple areal weighting, binary dasymetric mapping, and global regression methods are tested. Strengths and weaknesses of each technique are discussed. Our findings offer a new population estimate for the River Njoro watershed in Kenya and suggest that census data alone are inadequate for many resource management decisions. At the same time, however, care must be taken to account for the spatial characteristics of the environment when developing a model for improved estimates of population distribution.

Network approaches for understanding rainwater management from a social-ecological systems perspective

The premise of this research is to better understand how approaches to implementing rainwater management practices can be informed by understanding how the people living and working in agroecosystems are connected to one another. Because these connections are via both social interactions and functional characteristics of the landscape, a social-ecological network emerges. Using social-ecological network theory, we ask how understanding the structure of interactions can lead to improved rainwater management interventions. Using a case study situated within a small sub-basin in the Fogera area of the Blue Nile Basin of Ethiopia, we build networks of smallholders based both on the biophysical and social-institutional landscapes present in the study site, with the smallholders themselves as the common element between the networks. In turn we explore how structures present in the networks may serve to guide decision making regarding both where and with whom rainwater management interventions could be developed. This research thus illustrates an approach for constructing a social-ecological network and demonstrates how the structures of the network yield insights for tailoring the implementation of rainwater management practices to the social and ecological setting.

A hybrid evolutionary-graph approach for finding functional network paths

In this paper we consider the concept of functional paths through network datasets. Functional paths are paths that may be suboptimal in terms of cumulative edge weight, but in which the morphology of the route may serve a specific functional purpose (e.g., detection avoidance). Such routes may tend toward optimal in terms of minimizing for edge weight, but not at the expense of the functional purpose of the route. We present this class of routing problems and illustrate how evolutionary approaches used in conjunction with more traditional graph-based computation offers a great deal of flexibility in finding feasible solutions. Using both synthetic graphs and real-world road networks, we present a hybrid evolutionary and graph-based approach for discovering routes with specific functional characteristics. The presented evolutionary algorithm represents a novel solution to a challenging class of problems not readily solved by more traditional approaches.

Evolutionary search for understanding movement dynamics on mixed networks

This paper describes an approach to using evolutionary algorithms for reasoning about paths through network data. The paths investigated in the context of this research are functional paths wherein the characteristics (e.g., path length, morphology, location) of the path are integral to the objective purpose of the path. Using two datasets of combined surface and road networks, the research demonstrates how an evolutionary algorithm can be used to reason about functional paths. We present the algorithm approach, the parameters and fitness function that drive the functional aspects of the path, and an approach for using the algorithm to respond to dynamic changes in the search space. The results of the search process are presented in terms of the overall success based on the response of the search to variations in the environment and through the use of an occupancy grid characterizing the overall search process. The approach offers a great deal of flexibility over more conventional heuristic path finding approaches and offers additional perspective on dynamic network analysis.