Addressing the challenges of a quarter century of giscience education: A flexible higher education curriculum framework

A wide range of geographic information science (GIScience) educational programs currently exist, the oldest now over 25 years. Offerings vary from those specifically focussed on geographic information science, to those that utilise geographic information systems in various applications and disciplines. Over the past two decades, there have been a number of initiatives to design curricula for GIScience, including the NCGIA Core Curriculum, GIS&T Body of Knowledge and the Geospatial Technology Competency Model developments. The rapid developments in geospatial technology, applications and organisations have added to the challenges that higher educational institutions face in order to ensure that GIScience education is relevant and responsive to the changing needs of students and industry. This paper discusses some of the challenges being faced in higher education in general, and GIScience education in particular, and outlines a flexible higher education curriculum framework for GIScience

PGIS Supported Knowledge Based Participation and Evidences of Empowered Community Members

In much of Botswana’s rural population, formal business ethic is not common and the business environment contains fragmented and incomplete information. Under such mainly agrarian economies, the villagers do not know where the customers for their produce are, there is limited technology and often the farmers are not aware of the potential benefits from their agro-businesses. Many people are also not aware of the supportive policy frameworks, the agricultural programs/projects that the government has set up for them and the attendant financial support programs that are intended to implement the programs. The villagers participate more in social welfare programs from which they do not earn enough to live dignified lives. Participation and empowerment paradigms have been used in development programs to foster rural community development. However, many national and international development projects have been implemented with insufficient understanding of participation and empowerment processes. Using participation as learning and empowerment as informed participation within community group interactions, this paper presents the use of participatory action research implemented through participatory geographic information system (PGIS), to facilitate community learning and the construction of a PGIS based knowledge repository. The knowledge repository addressed issues of fragmented and incomplete information and also served more to facilitate knowledge construction, encourage local innovation and forged links with the local community development institutions as well as district and central government institutions

Web mapping for promoting interaction and collaboration in community land planning

There is an inherent advantage of geographic information Systems (GIS) and mapping in facilitating dialogue between experts and non-experts during land use plan development. Combining visual mapping information and effective user interaction can result in considerable benefits for developing countries like Botswana. Although the adoption of information and communication technologies has lagged behind that for developed countries, initiatives by the Botswana government in providing suitable information infrastructures, including internet and web based communications, are enabling multiple users to interact and collaborate in community land planning. A web mapping application was developed for the Maun Development Plan (MDP) in the Okavango Delta region in Botswana. It was designed according to requirements of land planners and managers and implemented using ArcGIS Viewer for Flex. Land planners and managers from two organisations in Maun involved in the development of the MDP were asked to evaluate the web mapping tools. This paper describes the results of implementation and some preliminary results of the web mapping evaluation

Cloud/web mapping and geoprocessing services - Intelligently linking geoinformation

We live in a world that is alive with information and geographies. ‘‘Everything happens somewhere” (Tosta, 2001). This reality is being exposed in the digital earth technologies providing a multidimensional, multi-temporal and multi-resolution model of the planet, based on the needs of diverse actors: from scientists to decision makers, communities and citizens (Brovelli et al., 2015). We are building up a geospatial information infrastructure updated in real time thanks to mobile, positioning and sensor observations. Users can navigate, not only through space but also through time, to access historical data and future predictions based on social and/or environmental models. But how do we find the information about certain geographic locations or localities when it is scattered in the cloud and across the web of data behind a diversity of databases, web services and hyperlinked pages? We need to be able to link geoinformation together in order to integrate it, make sense of it, and use it appropriately for managing the world and making decisions

Combining distance and face-to-face teaching and learning in spatial computations

Retention and passing rates as well as student engagement in computer programming and problem solving units are a major concern in tertiary spatial science courses. A number of initiatives were implemented to improve this. A pilot study reviews the changes made to the teaching and learning environment, including the addition of new resources and modifications to assessments, and investigates their effectiveness. In particular, the study focuses on the differences between students studying in traditional, oncampus mode and distance, e-learning mode. Student results and retention rates from 2009-2011, data from in-lecture clicker response units and two anonymous surveys collected in 2011 were analysed. Early results indicate that grades improved for engaged students but pass rates or grades of the struggling cohort of students did not improve significantly

Industry-driven criteria development of work-ready graduates in the Spatial Sciences

Work integrated learning (WIL) programs are being built into all Curtin University courses by 2016 and are becoming progressively more common in other tertiary institutions throughout Australia and elsewhere. To ensure they are effective at developing more employable, work-ready graduates, they will need to be tailored to industry requirements and expectations. To this end, the criteria of a work-ready graduate were identified from over 40 participants within industry for two closely related disciplines in the Department of Spatial Sciences (Surveying and Geographic Information Science - GISc). A modified Delphi process was subsequently used to test for consensus and eliminate poorly represented criteria. Preliminary results were analysed and differences between the disciplines were explored. The perceived skill-levels of a cohort of students were also analysed, based on online gathering of their reflection, before, during and at the end of their WIL program. These results were compared with judgments made by their work-place supervisors. Findings are used for a better design of WIL programs, including learning activities and assessment practices that address industry requirements and improve student satisfaction

Input parameters selection for soil moisture retrieval using an artificial neural network

Factors other than soil moisture which influence the intensity of microwave emission from the soil include surface temperature, surface roughness, vegetation cover and soil texture which make this a non-linear and ill-posed problem. Artificial Neural Networks (ANNs) have been demonstrated to be good solutions to this type of problem. Since an ANN is a data driven model, proper input selection is a crucial step in its implementation as the presence of redundant or unnecessary inputs can severely impair the ability of the network to learn the target patterns. In this paper, the input parameters are chosen in combination with the brightness temperatures and are based on the use of incremental contributions of the variables towards soil moisture retrieval. Field experiment data obtained during the National Airborne Field Experiment 2005 (NAFE'05) are used. The retrieval accuracy with the input parameters selected is compared with the use of only brightness temperature as input and the use of brightness temperature in conjunction with a range of available parameters. Note that this research does not aim at selecting the best features for all ANN soil moisture retrieval problems using passive microwave. The paper shows that, depending on the problem and the nature of the data, some of the data available are redundant as the input of ANN for soil moisture retrieval. Importantly the results show that with the appropriate choice of inputs, the soil moisture retrieval accuracy of ANN can be significantly improved

Applying cusum-based methods for the detection of outbreaks of Ross River virus disease in Western Australia

<p>Abstract</p> <p>Background</p> <p>The automated monitoring of routinely collected disease surveillance data has the potential to ensure that important changes in disease incidence are promptly recognised. However, few studies have established whether the signals produced by automated monitoring methods correspond with events considered by epidemiologists to be of public health importance. This study investigates the correspondence between retrospective epidemiological evaluation of notifications of Ross River virus (RRv) disease in Western Australia, and the signals produced by two cumulative sum (cusum)-based automated monitoring methods.</p> <p>Methods</p> <p>RRv disease case notification data between 1991 and 2004 were assessed retrospectively by two experienced epidemiologists, and the timing of identified outbreaks was compared with signals generated from two different types of cusum-based automated monitoring algorithms; the three Early Aberration Reporting System (EARS) cusum algorithms (C1, C2 and C3), and a negative binomial cusum.</p> <p>Results</p> <p>We found the negative binomial cusum to have a significantly greater area under the receiver operator characteristic curve when compared with the EARS algorithms, suggesting that the negative binomial cusum has a greater level of agreement with epidemiological opinion than the EARS algorithms with respect to the existence of outbreaks of RRv disease, particularly at low false alarm rates. However, the performance of individual EARS and negative binomial cusum algorithms were not significantly different when timeliness was also incorporated into the area under the curve analyses.</p> <p>Conclusion</p> <p>Our retrospective analysis of historical data suggests that, compared with the EARS algorithms, the negative binomial cusum provides greater sensitivity for the detection of outbreaks of RRv disease at low false alarm levels, and decreased timeliness early in the outbreak period. Prospective studies are required to investigate the potential usefulness of these algorithms in practice.</p

Use of Soil Moisture Variability in Artificial Neural Network Retrieval of Soil Moisture

Passive microwave remote sensing is one of the most promising techniques for soil moisture retrieval. However, the inversion of soil moisture from brightness temperature observations is not straightforward, as it is influenced by numerous factors such as surface roughness, vegetation cover, and soil texture. Moreover, the relationship between brightness temperature, soil moisture and the factors mentioned above is highly non-linear and ill-posed. Consequently, Artificial Neural Networks (ANNs) have been used to retrieve soil moisture from microwave data, but with limited success when dealing with data different to that from the training period. In this study, an ANN is tested for its ability to predict soil moisture at 1 km resolution on different dates following training at the same site for a specific date. A novel approach that utilizes information on the variability of soil moisture, in terms of its mean and standard deviation for a (sub) region of spatial dimension up to 40 km, is used to improve the current retrieval accuracy of the ANN method.A comparison between the ANN with and without the use of the variability information showed that this enhancement enables the ANN to achieve an average Root Mean Square Error (RMSE) of around 5.1% v/v when using the variability information, as compared to around 7.5% v/v without it. The accuracy of the soil moisture retrieval was further improved by the division of the target site into smaller regions down to 4 km in size, with the spatial variability of soil moisture calculated from within the smaller region used in the ANN. With the combination of an ANN architecture of a single hidden layer of 20 neurons and the dual-polarized brightness temperatures as input, the proposed use of variability and sub-region methodology achieves an average retrieval accuracy of 3.7% v/v. Although this accuracy is not the lowest as comparing to the research in this field, the main contribution is the ability of ANN in solving the problem of predicting “out-of-range” soil moisture values. However, the applicability of this method is highly dependent on the accuracy of the mean and standard deviation values within the sub-region, potentially limiting its routine application