ABSTRACTThis article presents a study evaluating student perceptions of learning experiences creating ArcGIS StoryMaps. The Taxonomy of Educational Objectives (Bloom's Taxonomy) provides a framework for organising, analysing, and interpreting a survey completed by 154 students from high school to postgraduate. Using a mix of quantitative and qualitative methods we found student perceptions of the level of learning to be similar across education cohorts in the cognitive domain, exhibit some differences in psychomotor domain, and are markedly different in the affective domain. In terms of what they learned, postgraduates perceive the experience as being mostly about communicating ideas or storytelling more than undergraduates who, in addition, tend to see the experience as preparing maps and working with geographic information systems. Undergraduates tend to conflate gaining knowledge (cognitive) with learning skills (psychomotor), suggesting a greater focus on working with the technology. Most students across all education levels found the learning experience challenging and beneficial (affective) though some students did not. We conclude that while the creation of StoryMaps is good for student retention and offers an effective scaffolding framework for geography and GIS education, it is important for educators to design StoryMap learning activities using clearly defined learning outcomes and assessment rubrics.KEYWORDS: Digital storytellingdigital literaciesstudent perceptionsGIS educationaffective learningTaxonomy of educational objectives AcknowledgementsWe thank the students who contributed to this work by participating in the survey. We are grateful to the anonymous reviewers of the original manuscript for their thoughtful comments that helped improve the quality of this paper.Disclosure statementThere are no relevant financial or non-financial competing interests to report.Ethics statement and informed consentApproval for conducting the survey was approved by Massey University Ethics Committee, through application SOB 21/41. All students who participated in the survey offered informed consent.

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10.1080/03098265.2023.2255535

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Quantifying indicators of riparian condition in Australian tropical savannas: integrating high spatial resolution imagery and field survey data

International Journal of Remote Sensing (2008)

Kasper Johansen Stuart Phinn John H. Lowry Michael M. Douglas

K. Johansena*, S. Phinna, J. Lowryb & M. Douglasc a Centre for Remote Sensing and Spatial Information Science , School of Geography , Planning and Architecture , The University of Queensland , Brisbane, QLD 4072, Australia b Supervising Scientist Division , Department of Environment and Heritage , PO Box 461, Darwin, NT 0801, Australia c Tropical Rivers and Coastal Knowledge (TRACK) Research Hub , Charles Darwin University , Darwin, NT 0909, Australia

10.1080/01431160802220201

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Spatiotemporal analysis of forest cover change and associated environmental challenges: a case study in the Central Highlands of Vietnam

Geocarto International (2021)

Duy X. Tran Thuong V. Tran Diane Pearson Soe W. Myint John H. Lowry

Spatiotemporal regression combining Theil-Sen median trend and Man-Kendall tests was applied to MODIS time-series data to quantify the trend and rate of change to forest cover in the Central Highlands, Vietnam from 2001 to 2019. Several MODIS data products, including Percent Tree Cover (PTC), Evapotranspiration (ET), Land Surface Temperature (LST), and Gross Primary Productivity (GPP) were selected as indicators for forest cover and climate and carbon cycle patterns. Emerging hot spot analysis was applied to identify patterns of long-term deforestation. Spatial regression analysis using Geographically Weighted Regression (GWR) was performed to understand variations in the relationship between vegetation changes and trends in LST, ET, and GPP. Our analysis reveals that deforestation occurred significantly in the study area with a total decrease of 14.5% in PTC and a total of 7314 deforestation hot spots were identified. Results indicate that forest cover loss explains 72.9%, 67.7%, and 89.4% of the changes in ET, GPP, and LST, respectively, and the levels of influence are heterogenous across space and dependent on the types of deforestation hot spots. The approach introduced in our study can be performed worldwide to address complex research questions about environmental challenges that emerge from deforestation.

Deforestation

Forest cover

Land Cover

Trend analysis

10.1080/10106049.2021.2017013

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Effects of spatial data resolution on the modelling and mapping of soil organic carbon content in hill country grassland landscapes

Soil Use and Management (2023)

Duy X. Tran Estelle Dominati John H. Lowry A. D. Mackay Ronaldo Vibart

Abstract Limited use has been made of spatially explicit modelling of soil organic carbon (SOC) in highly complex farmed landscapes to advance current mapping efforts. This study aimed to address this gap in knowledge by evaluating the spatial prediction of SOC content in the 0–75 mm soil depth in hill country landscapes in New Zealand (NZ) using point‐based training data, along with topographic covariates and Sentinel 2 spectral band ratios using an automated set of machine learning (AutoML) tools in ArcGIS. Subsequently, it also focused on quantifying the effects of spatial data resolution (i.e., 1, 8, 15, and 25 m) in terms of predicted map accuracy. Farmlets with contrasting phosphorus fertilizer and sheep grazing histories located at the Ballantrae Hill Country Research Station, NZ were selected to conduct the research. Six candidate algorithms incorporated in the AutoML tools (i.e., XGBoost, LightGBM, linear regression, decision trees, extra trees, random forest) and ensemble model were utilized to model the spatial pattern of SOC content. The results show that the ensemble model that combine predictions of various algorithms applied for 1 m data resolution enables the highest performance and accuracy (i.e., R 2 = .76, RMSE = 0.66%). Among the predictive variables used in the model, slope, wetness, and topographic position indices were found to be the most important topographical features that explain SOC patterns in the study area. Inclusion of spectral indices derived from remote sensing, including surface soil moisture and clay minerals ratio, made further improvement to the SOC content prediction. The study reveals that a decrease in the resolution of the geospatial data does not substantively affect the mean SOC content estimation of a farm‐scale modelling. However, using coarser resolution data reduces the ability of the model to predict changes in the spatial pattern of SOC content across a hill country grassland landscape.

Soil carbon

Content (measure theory)

Carbon fibers

10.1111/sum.12966

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Use of geographically weighted logistic regression to quantify spatial variation in the environmental and sociodemographic drivers of leptospirosis in Fiji: a modelling study

The Lancet Planetary Health (2018)

Helen J. Mayfield John H. Lowry Conall Watson Mike Kama Eric J. Nilles

Summary

Background

Leptospirosis is a globally important zoonotic disease, with complex exposure pathways that depend on interactions between human beings, animals, and the environment. Major drivers of outbreaks include flooding, urbanisation, poverty, and agricultural intensification. The intensity of these drivers and their relative importance vary between geographical areas; however, non-spatial regression methods are incapable of capturing the spatial variations. This study aimed to explore the use of geographically weighted logistic regression (GWLR) to provide insights into the ecoepidemiology of human leptospirosis in Fiji.

Methods

We obtained field data from a cross-sectional community survey done in 2013 in the three main islands of Fiji. A blood sample obtained from each participant (aged 1–90 years) was tested for anti-Leptospira antibodies and household locations were recorded using GPS receivers. We used GWLR to quantify the spatial variation in the relative importance of five environmental and sociodemographic covariates (cattle density, distance to river, poverty rate, residential setting [urban or rural], and maximum rainfall in the wettest month) on leptospirosis transmission in Fiji. We developed two models, one using GWLR and one with standard logistic regression; for each model, the dependent variable was the presence or absence of anti-Leptospira antibodies. GWLR results were compared with results obtained with standard logistic regression, and used to produce a predictive risk map and maps showing the spatial variation in odds ratios (OR) for each covariate.

Findings

The dataset contained location information for 2046 participants from 1922 households representing 81 communities. The Aikaike information criterion value of the GWLR model was 1935·2 compared with 1254·2 for the standard logistic regression model, indicating that the GWLR model was more efficient. Both models produced similar OR for the covariates, but GWLR also detected spatial variation in the effect of each covariate. Maximum rainfall had the least variation across space (median OR 1·30, IQR 1·27–1·35), and distance to river varied the most (1·45, 1·35–2·05). The predictive risk map indicated that the highest risk was in the interior of Viti Levu, and the agricultural region and southern end of Vanua Levu.

Interpretation

GWLR provided a valuable method for modelling spatial heterogeneity of covariates for leptospirosis infection and their relative importance over space. Results of GWLR could be used to inform more place-specific interventions, particularly for diseases with strong environmental or sociodemographic drivers of transmission.

Funding

WHO, Australian National Health & Medical Research Council, University of Queensland, UK Medical Research Council, Chadwick Trust.

Odds

10.1016/s2542-5196(18)30066-4

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