Because history is inaccessible to experimentation, agent-based and other simulations are a main source to explore theories about pre-historical humanity. Continent-scale migrations are of great interest in this context. With advances in computing and GIS, tracking entire populations migrating across continents become accessible in simulation. In this paper, I present a network representing North and South America for such tasks. The nodes roughly follow a hexagonal grid and represent small territories around a focal point. They are annotated with the carrying capacity for hunter-gatherers per ecoregion in the vicinity. The edge weights represent the travel times between the focal points on foot or by boat. I validate the network by comparing its predicted optimal path between Nashville, TN and Natchez, MI with the route of the historical Natchez Trace.
Virtual reality technology has the potential to be a revolutionary addition to the field of Geographic Information Science. The application of virtual reality to GIScience has been discussed for decades, however adoption has been limited until recently. Virtual reality GIScience represents an interdiscip- linary approach, incorporating fields such as video game development. In this paper, we introduce Locative Reality, a virtual reality software that presents users with immersive 360° video experiences of forest environments. It incorporates spatial information into the virtual environment so that data generated by virtual research can be directly linked to real-world locations. The implications for the field of GIScience include virtual research tools and educational experiences, accessible to anyone anywhere in virtual reality.
Even if we are not aware, our emotions can influence and interplay with our navigation and use of mobile navigation aids. A given map display can make us feel good by reminding us of pleasant past experiences, or it can make us feel frustrated because we are not able to understand the information provided. Navigation aids could also make a given landmark emotionally charged, and thus more salient and memorable for a navigator, for example, by using an auditory narrative containing emotional cues. By storytelling, it would also be possible to provide details about a given landmark and connect proximal landmarks to each other. But how do navigational instructions in the form of emotional storytelling affect spatial memory and map use? Results from a preliminary study indicated that a video presentation viewed from a first person perspective is looked at more often than an abstract map, and this evidence becomes even stronger when instructions are emotionally laden. We discuss results in the context of place meaning and how emotions’ role in navigation should be further assessed, in particular to increase spatial learning from navigation aids.
Data related to households or addresses needs be published in an aggregated form to obfuscate sensitive information about individuals. Usually, the data is aggregated to the level of existing administrative zones, but these often do not correspond to formal models of privacy or a desired level of anonymity. Therefore, automatic privacy-preserving spatial clustering methods are needed. To address this need, we present algorithms to partition a given set of locations into k-anonymous clusters, meaning that each cluster contains at least k locations. We assume that the locations are given as a set T ⊆ V of terminals in a weighted graph G = (V, E) representing a road network. Our approach is to compute a forest in G, i.e., a set of trees, each of which corresponds to a cluster. We ensure the k-anonymity of the clusters by constraining the trees to span at leastterminals each (plus an arbitrary number of non-terminal nodes called Steiner nodes). By minimizing the total edge weight of the forest, we ensure that the clusters reflect the proximity among the locations. Although the problem is NP-hard, we were able to solve instances of several hundreds of terminals using integer linear programming. Moreover, we present an efficient approximation algorithm and show that it can be used to process large and fine-grained data sets.
Spatio-racial distributions in major US cities change on the timescale of a single decade. Here we describe a methodology to forecast such changes a decade ahead. First, we transform the data from population counts to a grid of categorical population types. Then, we build an empirical model of past change using supervised machine learning and extrapolate it into the future to make a prediction. The model uses only statistics of population categories as features, there are no ancillary variables. To account for the non-stationarity of the change we use a synthetic training dataset based on past transitions and estimated future frequencies of these transitions. The methodology is described and validated by training a model on 1990-2000 data and using it to predict spatio-racial distributions in 2010. This prediction is then compared to the actual spatio-racial 2010 distribution. We have found that a highly accurate model of change can be constructed using this methodology. Extrapolating such models to the future results in some loss of accuracy, but the method still yields satisfactory predictions.
Polycentricity is a critical characteristic of the spatial organization of cities. Many indices have been proposed to measure the degree of morphological polycentricity or functional polycentricity. However, selecting a proper set of polycentricity indices for cities in a particular region or country still needs prior expert knowledge. This study demonstrates that whole graph embedding, as a novel and efficient computational tool, can model the city polycentricity in an integrated manner without much prior knowledge. The new method can further support visual analytics and classification very well.
Landmarks serve to structure the environment we experience, and therefore they are also critically important for our everyday movement through and knowledge acquisition about space. How to effectively visualize landmarks to support spatial learning during map-assisted pedestrian navigation is still an open question. We thus set out to assess how landmark visualization styles (i.e., abstract 2D vs. realistic 3D) influence map-assisted spatial learning of expert wayfinders in an outdoor navigation study. Below we report on how the visualization of landmarks on mobile maps might influence wayfinder’s gaze behavior while trying to find a set of landmarks along a given route in an unfamiliar environment. We find that navigators assisted with mobile maps showing realistic-looking 3D landmarks more equally share their visual attention on task-relevant information, while those assisted with maps containing abstract 2D landmarks frequently switch their visual attention between the visualized landmarks and the mobile map to complete the navigation task. The presented analysis approach for the assessment of wayfinder’s gaze patterns has the potential to contribute ecologically valid insights for the understanding of human visual attention allocation during outdoor navigation, and to further understand how landmark depiction styles on mobile maps might guide wayfinders’ visual attention back to the environment to support spatial learning during map-assisted navigation.
Superpixels are a promising group of techniques allowing for generalization of spatial information. Among this group, the Simple Linear Iterative Clustering (SLIC) superpixels algorithm proved to be first-rate, both in terms of the quality of the output and the performance. SLIC, however, is limited to detecting homogeneous areas and uses the Euclidean distance only. Here, we propose an extension of SLIC allowing to use any specified distance measure for single or multi-layered spatial raster data. To present our idea, we use the extension to create an over-segmentation of areas with similar proportions of different land cover categories in Ohio. Given a proper distance measure, the proposed extension can also be used for other scenarios, including creating regions of similar temporal patterns or similarly ranked areas. Depending on the use case, the resulting superpixels could be either the result of the analysis or the input for further classification or clustering.
Recently, major cities are facing air pollution problems mostly caused by individual car traffic. Besides the emission of greenhouse gases, particulate matter is a particular concern for public health. In order to mitigate these emission related issues, we developed an environmentally friendly routing approach, which calculates the most fuel-efficient route - based on the driving dynamics of the road, vehicle, and traffic characteristics. In addition, the calculated route is designed to avoid regions of high particulate matter concentration. In order to integrate real-time air quality data of moving and stationary sensors using OGC Sensor Observation Service. Cars are used as moving sensors in the city. The paper evaluates the effects of air quality (particulate matter & greenhouse gases) on the route calculation - so that cars/bikes may receive real-time recommendations to avoid polluted areas.
We address the concept of embodied digital twins of real-world forest environments to support research, education, communication, and decision-making. We discuss approaches to generate these kinds of immersive experiences and how to link them to ecological models. We then present the prototype of an iVR embodied digital twin intended as an interactive workbench for analyzing remotely sensed forest data. Lastly, we discuss challenges for future work in this area.
