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ABSTRACTButtenfield (1988) pioneered research on multiple representations in the dawn of GIScience. Her efforts evoked inquiries into fundamental issues arising from the selective abstractions of infinite geographic complexity in spatial databases, cartography, and application needs for varied geographic details. These fundamental issues posed ontological challenges (e.g. entity classification) and implementational complications (e.g. duplication and inconsistency) in geographic information systems (GIS). Expanding upon Buttenfield’s line of research over the last three decades, this study reviewed multiple representations in spatial databases, spatial cognition, and deep learning. Initially perceived as a hindrance to GIS, multiple representations were found to offer new perspectives to encode and decipher geographic complexity. This paper commenced by acknowledging Buttenfield’s pivotal contributions to multiple representations in GIScience. Subsequent discussions synthesized the literature to outline cognitive representations of space in the brain’s hippocampal formation and feature representations in deep learning. By cross-referencing related concepts of multiple representations in GIScience, the brain’s spatial cells, and machine learning algorithms, this review concluded that multiple representations facilitate learning geography for both humans and machines.

Given the importance of online word of mouth (WOM), there has been an increasing need to understand the psychological mechanisms that underlie WOM transmission (i.e. sharing of opinions) and reception (i.e. processing of received messages). The goal of the current paper is to review some of the most recent research in online WOM (focusing on the past two to four years) as well as make suggestions regarding future research. [For earlier syntheses on WOM senders and social media marketing, see King et al., 2014, Stephen, 2016, Whitler, 2014] [6–8].

ABSTRACTThe contrast of space and place has long been an active topic of scholarly discussions in many disciplines. While spatial analysis enjoys a multitude of quantitative methods, the study of place remains mostly conceptual and descriptive. This paper expands upon the rich concepts of place in the literature to propose a quantitative framework for placial analysis based on events. Central to the proposed framework are three assumptions: (1) human experiences transform space to place; (2) events build human experiences in space; and (3) places emerge organically and may change characters, spatial extent and location over time through the shifts in occurrences and types of events in space and time. The proposed framework consists of three elements: clustering events, decomposing event distributions, and identifying the similarity of event clusters. We applied the framework to identify criminological places in the City of Dallas in the United States and the changes of these places from 1 June 2014 to 30 May 2018.

ABSTRACTWe propose a novel approach to network-based event likelihood modeling that estimates the probabilities of event occurrences on a network and identifies the influences of site and situation characteristics. Our premise is that the occurrences of events that involve human activities are subject to site and situational characteristics, and an understanding of event occurrences serves the basis for preparation or mitigation. Using data from Dallas, Texas, USA, we take the proposed approach to estimate the likelihood of traffic accidents based on binary (event or nonevent) space–time atoms of 100-m road segments and 1-h intervals. We choose 12 variables representing time, site characteristics, and situational conditions based on literature reviews to develop logistic regression and random forest models. The traffic accident data on even days were used for model construction and data on odd days for model testing. Both models result in comparable accuracy at 84.11% (logistic regression) and 85.42% (random forest) with significant differences in the spatial patterns of how site and situation correlate to traffic accidents. The difference signals the dynamic influence of site and situation characteristics on the event likelihood over time. The proposed approach shall be applicable to other point events on a network.

AbstractImproving solar radiation models is critical for supporting the increase in solar energy usage and modeling ecosystem dynamics. However, coarse spatial resolutions of solar radiation models overlook the impacts resulting from spatial variability of clouds at meso- and micro-scales. To address this problem, Moderate Resolution Imaging Spectroradiometer (MODIS) cloud climatology developed by the National Severe Storms Laboratory was used to relate cloudiness to surface solar radiation observations. We developed a linear regression model between the surface solar radiation and MODIS cloud climatology and used the model to estimate average radiation across Oklahoma. Furthermore, the study compared the average error and coefficient of determination to measured ground radiation. Error analysis of the regression model showed that the differences between observed radiation and estimated radiation were spatially autocorrelated for the Aqua MODIS satellite scan. This suggests cloudiness alone is not sufficient to predict surface solar radiation. This study found that simple cloud datasets alone can account for approximately 50% of the variation in observed solar radiation at 250-m spatial resolution, but additional datasets such as optical depth, elevation, and slope are needed to accurately explain spatial distributions of incoming shortwave radiation.

Results are compared of using arithmetic averaging and kriging to estimate daily area-average rainfall during the summer 1991 Convection and Precipitation/Electrification Experiment (CaPE) in central Florida, where convective rainstorms often produce rain-fields of high spatial variability. Four kriging methods were applied: ordinary kriging using exponential and spherical semivariograms and universal kriging using linear and quadratic trends. The following results were obtained. First, the differences between daily area-average rainfall estimated by arithmetic averaging and kriging are statistically insignificant when the arithmetic mean is less than 12 mm. Second, for days with daily arithmetic means. Third, kriging estimates for daily area-average rainfall are insensitive to spatial resolution among grid spacing varying from 500 m to 20 km. Fourth, universal kriging with quadratic trends provides the best estimate of daily rainfall at a given location among the four kriging methods. [Key words: area-average rainfall, kriging, CaPE, Florida.]

Conventionally, spatial data models have been designed according to object- or field-based conceptualizations of reality. Conceptualization of complex geographic phenomena that have both object- and field-like properties, such as wildfire and precipitation, has not yet been incorporated into GIS data models. To this end, a new conceptual framework is proposed in this research for organizing data about such complex geographic phenomena in a GIS as a hierarchy of events, processes, and states. In this framework, discrete objects are used to show how events and processes progress in space and time, and fields are used to model how states of geographic themes vary in a space-time frame. Precipitation is used to demonstrate the construction and application of the proposed framework with digital precipitation data from April 15 to May 22, 1998, for the state of Oklahoma, U.S.A. With the proposed framework, two sets of algorithms have been developed. One set automatically assembles precipitation events and processes from the data and stores the precipitation data in the hierarchy of events, processes, and states, so that attributes about events, processes, and states are readily available for information query. The other set of algorithms computes information about the spatio-temporal behavior and interaction of events and processes. The proposed approach greatly enhances support for complex spatio-temporal queries on the behavior and relationships of events and processes.

The article discusses novel strategies for educators and school staff for integrating social media and Social emotional learning (SEL) for students. Topics include COVID-19 pandemic has sparked a digital revolution in our society and educational systems; and use of the term social media also refers to all digital media platforms used for social interactions, and sharing content.

Wildfire provides a good example to study spatio-temporal representations for GIS applications because of its spatio-temporal variability. One of the salient features ofa GIS is itsbuilt-in capability to calculate and maintain centroid, length, area, and topological relations among spatial objects. Likewise, a competent temporal GIS should be able to support queries about temporal measures, such as rate, frequency, life expectancy, and temporal relations. In doing so, temporal GIS need well-designed representations to structure spatiotemporal constructs and their relations. While temporal information is often incorporated into GIS by time-stamping attributes or layers, this research applies knowledge acquisition techniques of written materials analysis and unstructured interviews with wildfire professionals to elicit conceptual models for wildfire representation. Four elicited conceptual models form a wildfire information cycle which presents a continuum in wildfire conceptualization across different types of wildfire studies. Current GIS layer models can support three of the four conceptual models: locational snapshots (as raster layers), entity snapshots (as features on vector layers), and fire mosaics (as vector layers). The support of layer-based models for representing fire processes is, however, inadequate. With the wildfire information cycle and its four conceptual models, this study shows (1) spatio-temporal information dependency among human's conceptual models, (2) the missing process-oriented data model (fire entities) in GIS, and (3) a three-domain model of semantics, time, and space as a generic GIS representation to support all the four conceptual models. Follow-up studies are undertaken in formulating the three-domain model to implement the wildfire information cycle.