Student Learning in an Introductory GIS Course: Using a Project‐Based Approach

Introductory courses in Geographic Information Science (GIS) expose students to the concepts and practices necessary for future academic and professional use of GIS tools. Traditional GIS courses balance lectures in the theories of GIS with pre‐built and pre‐packaged lab activities to learn the practices of GIS. This article presents a case study of an experimental introductory course in which students conducted novel, independent project‐based group research under the supervision of graduate or advanced undergraduate students enrolled in the course, culminating in a class presentation and publication quality paper. Surveys and interviews indicated that students reacted more positively to project‐based group research than to traditional activities. Students felt the projects better prepared them for ‘real world’ applications of GIS, and recommend project‐based group research in other GIS coursework. Additionally, our findings indicate that students appreciate interactions with peers of varying skill levels and experiences, as these broaden their capabilities to work with other GIS users.     

A hybrid approach to modeling territorial control in violent armed conflicts

Territorial control is central to the understanding of violent armed conflicts, yet reliable and valid measures of this concept do not exist. We argue that geospatial analysis provides an important perspective to measure the concept. In particular, measuring territorial control can be seen as an application of calculating service areas around points of control. The modeling challenge is acute for areas with limited road infrastructure, where no complete network is available to perform the analysis, and movements largely occur off road. We present a new geospatial approach that applies network analysis on a hybrid transportation network with both actual road data and hexagon‐fishnet‐based artificial road data representing on‐road and off‐road movements, respectively. Movement speed or restriction can be readily adjusted using various input data. Simulating off‐road movement with hexagon‐fishnet‐based artificial road data has a number of advantages including scalability to small or large study areas and flexibility to allow all‐directional travel. We apply this method to measuring territorial control of armed groups in Sub‐Saharan Africa where inferior transport infrastructure is the norm. Based on the Uppsala Conflict Data Program's (UCDP) Georeferenced Event Data (GED) as well as spatial data on terrain, population locations, and limited transportation networks, we enhance the delineation of the specific areas directly controlled by each warring party during civil wars within a given travel time.     

Spatial process and data models: Toward integration of agent-based models and GIS

The use of object-orientation for both spatial data and spatial process models facilitates their integration, which can allow exploration and explanation of spatial-temporal phenomena. In order to better understand how tight coupling might proceed and to evaluate the possible functional and efficiency gains from such a tight coupling, we identify four key relationships affecting how geographic data (fields and objects) and agent-based process models can interact: identity, causal, temporal and topological. We discuss approaches to implementing tight integration, focusing on a middleware approach that links existing GIS and ABM development platforms, and illustrate the need and approaches with example agent-based models.     

A probabilistic framework for improving reverse geocoding output

Reverse geocoding, which transforms machine‐readable GPS coordinates into human‐readable location information, is widely used in a variety of location‐based services and analysis. The output quality of reverse geocoding is critical because it can greatly impact these services provided to end‐users. We argue that the output of reverse geocoding should be spatially close to and topologically correct with respect to the input coordinates, contain multiple suggestions ranked by a uniform standard, and incorporate GPS uncertainties. However, existing reverse geocoding systems often fail to fulfill these aims. To further improve the reverse geocoding process, we propose a probabilistic framework that includes: (1) a new workflow that can adapt all existing address models and unitizes distance and topology relations among retrieved reference data for candidate selections; (2) an advanced scoring mechanism that quantifies characteristics of the entire workflow and orders candidates according to their likelihood of being the best candidate; and (3) a novel algorithm that derives statistical surfaces for input GPS uncertainties and propagates such uncertainties into final output lists. The efficiency of the proposed approaches is demonstrated through comparisons to the four commercial reverse geocoding systems and through human judgments. We envision that more advanced reverse geocoding output ranking algorithms specific to different application scenarios can be built upon this work.     

Spatially explicit models for exploring COVID‐19 lockdown strategies

This article describes two spatially explicit models created to allow experimentation with different societal responses to the COVID‐19 pandemic. We outline the work to date on modeling spatially explicit infective diseases and show that there are gaps that remain important to fill. We demonstrate how geographical regions, rather than a single, national approach, are likely to lead to better outcomes for the population. We provide a full account of how our models function, and how they can be used to explore many different aspects of contagion, including: experimenting with different lockdown measures, with connectivity between places, with the tracing of disease clusters, and the use of improved contact tracing and isolation. We provide comprehensive results showing the use of these models in given scenarios, and conclude that explicitly regionalized models for mitigation provide significant advantages over a “one‐size‐fits‐all” approach. We have made our models, and their data, publicly available for others to use in their own locales, with the hope of providing the tools needed for geographers to have a voice during this difficult time.     

A hierarchical indexing strategy for optimizing Apache Spark with HDFS to efficiently query big geospatial raster data

ABSTRACT

Earth observations and model simulations are generating big multidimensional array-based raster data. However, it is difficult to efficiently query these big raster data due to the inconsistency among the geospatial raster data model, distributed physical data storage model, and the data pipeline in distributed computing frameworks. To efficiently process big geospatial data, this paper proposes a three-layer hierarchical indexing strategy to optimize Apache Spark with Hadoop Distributed File System (HDFS) from the following aspects: (1) improve I/O efficiency by adopting the chunking data structure; (2) keep the workload balance and high data locality by building the global index (k-d tree); (3) enable Spark and HDFS to natively support geospatial raster data formats (e.g., HDF4, NetCDF4, GeoTiff) by building the local index (hash table); (4) index the in-memory data to further improve geospatial data queries; (5) develop a data repartition strategy to tune the query parallelism while keeping high data locality. The above strategies are implemented by developing the customized RDDs, and evaluated by comparing the performance with that of Spark SQL and SciSpark. The proposed indexing strategy can be applied to other distributed frameworks or cloud-based computing systems to natively support big geospatial data query with high efficiency.     

A meta-analysis of global crop water productivity of three leading world crops (wheat,corn, and rice) in the irrigated areas over three decades

ABSTRACT

The overarching goal of this study was to perform a comprehensive meta-analysis of irrigated agricultural Crop Water Productivity (CWP) of the world’s three leading crops: wheat, corn, and rice based on three decades of remote sensing and non-remote sensing-based studies. Overall, CWP data from 148 crop growing study sites (60 wheat, 43 corn, and 45 rice) spread across the world were gathered from published articles spanning 31 different countries. There was overwhelming evidence of a significant increase in CWP with an increase in latitude for predominately northern hemisphere datasets. For example, corn grown in latitude 40–50° had much higher mean CWP (2.45?kg/m³) compared to mean CWP of corn grown in other latitudes such as 30–40° (1.67?kg/m³) or 20–30° (0.94?kg/m³). The same trend existed for wheat and rice as well. For soils, none of the CWP values, for any of the three crops, were statistically different. However, mean CWP in higher latitudes for the same soil was significantly higher than the mean CWP for the same soil in lower latitudes. This applied for all three crops studied. For wheat, the global CWP categories were low (≤0.75?kg/m³), medium (>0.75 to <1.10?kg/m³), and high CWP (≥1.10?kg/m³). For corn the global CWP categories were low (≤1.25?kg/m³), medium (>1.25 to ≤1.75?kg/m³), and high (>1.75?kg/m³). For rice the global CWP categories were low (≤0.70?kg/m³), medium (>0.70 to ≤1.25?kg/m³), and high (>1.25?kg/m³). USA and China are the only two countries that have consistently high CWP for wheat, corn, and rice. Australia and India have medium CWP for wheat and rice. India’s corn, however, has low CWP. Egypt, Turkey, Netherlands, Mexico, and Israel have high CWP for wheat. Romania, Argentina, and Hungary have high CWP for corn, and Philippines has high CWP for rice. All other countries have either low or medium CWP for all three crops. Based on data in this study, the highest consumers of water for crop production also have the most potential for water savings. These countries are USA, India, and China for wheat; USA, China, and Brazil for corn; India, China, and Pakistan for rice. For example, even just a 10% increase in CWP of wheat grown in India can save 6974 billion liters of water. This is equivalent to creating 6974 lakes each of 100?m³ in volume that leads to many benefits such as acting as ‘water banks’ for lean season, recreation, and numerous ecological services. This study establishes the volume of water that can be saved for each crop in each country when there is an increase in CWP by 10%, 20%, and 30%.     

A bridge over troubled water: A contextual analysis of social vulnerability to climate change in a riverine landscape in south-east Norway

Local communities in the Gudbrandsdalen region in Norway are increasingly exposed to climate-induced hazards such as floods and landslides. A core question is how community members respond to climate change and what factors contribute to more resilient communities. The authors used a contextual approach to analyze data from semi-structured interviews along five dimensions. In Gaustad Muncipality they found that individuals’ motivation to adapt to climate change depended largely on subjective values such as identity, place attachment, cultural values, and social networks among individuals, which means it is crucial that strategic plans for adaptation to climate change at different policy levels are experienced as relevant by community members. While the studied community has experienced heavy floods in river systems and streams, little evidence of adaptation was observed. Instead, they appeared to adopt coping strategies. Landowners may have limited incentives to adapt to climate change due to contraproductive policy measures such as economic compensation for direct losses without requiring improved practices. Effective adaptation to climate change on the local level is likely to require making compensation mechanisms contingent upon landowners showing willingness to change from coping to adaptive practices, as well as a contextualized approach integrating local and scientific forms of knowledge.     

The application of remote sensing to environmental archaeology

An area in the Fenlands of Eastern England was used to assess the spectral, spatial, and seasonal requirements of airborne multispectral data for identifying wetland archaeological features by detecting crop and soil marks. Ordination of data from a scanner with 11 spectral channels was achieved using the Sheffield method, which calculates the wavebands that produce a 3 band composite image with optimum contrast using band variances and interband correlations. the spatial detail requirements for multispectral data were investigated by applying an edge enhancing filter to single waveband images. the loss of feature clarity with increased levels of spatial smoothing is visually apparent. Seasonal variation in crop mark visibility was quantified by cross tabulating feature visibility in fields known to contain features with time period and sensor type. If remote sensing is to be used for repeated operational archaeological survey then the quality and quantity of information which can be gained from the data must be evaluated. This study attempts to define some of the necessary operational requirements for the use of multispectral data for archaeology.     

A Raman spectroscopic study on the structural disorder of monazite–(Ce)

This study addresses whether Raman spectra can be used to estimate the degree of accumulated radiation damage in monazite-(Ce) samples whose chemical composition was previously determined. Our results indicate that the degree of disorder in monazite–(Ce), as observed from increasing Raman band broadening, generally depends on both the structural state (i.e., radiation damage) and the chemical composition (i.e., incorporation of non-formula elements). The chemical effects were studied on synthetic orthophosphates grown using the Li-Mo flux method, and non radiation-damaged analogues of the naturally radiation-damaged monazite–(Ce) samples, produced by dry annealing. We found that the “chemical” Raman-band broadening of natural monazite–(Ce) can be predicted by the empirical formula, $$ {\hbox{FWHM}} {\hbox{[c}}{{\hbox{m}}^{ - {1}}}{]} = {3}{.95} + {26}{.66} \times {\hbox{(Th}} + {\hbox{U}} + {\hbox{Ca}} + {\hbox{Pb)}} {\hbox{[apfu]}} $$ where, FWHM = full width at half maximum of the main Raman band of monazite–(Ce) (i.e., the symmetric PO₄ stretching near 970?cm^?1), and (Th+U+Ca+Pb) = sum of the four elements in apfu (atoms per formula unit). Provided the chemical composition of a natural monazite–(Ce) is known, this “chemical band broadening” can be used to estimate the degree of structural radiation damage from the observed FWHM of the ν₁(PO₄) band of that particular sample using Raman spectroscopy. Our annealing studies on a wide range of monazite–(Ce) reference materials and other monazite–(Ce) samples confirmed that this mineral virtually never becomes highly radiation damaged. Potential advantages and the practical use of the proposed method in the Earth sciences are discussed.