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.
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.
The article presents the scientific infrastructure in the field of GIScience in Poland. It shows the history of the development of the discipline, key research topics, academic and research units, and the scope of national and international scientific cooperation.
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.
Accurate and up-to-date land use maps are important to the study of human-environment interactions, urban morphology, environmental justice, etc. Traditional land use mapping approaches involve several surveys and expert knowledge of the region to be mapped. While traditional approaches generate accurate and authoritative maps, it is expensive and takes a long time to develop a new version of map. Besides, such maps have region-specific spatial embedding, making them difficult to benchmark and compare against other land use maps. This work introduces a scalable POI-based land use modeling approach to generate global land use maps at multiple spatial scales and different semantic granularities. In addition, our land use maps adhere to a unified land use categories and can be compared for accuracy and precision.
Public transportation in cities is less popular than the private car due to lower personal flexibility, perceived comfort or the unavailability of infrastructure. The latter one is an issue in Augsburg with regard to outer districts since the existing star-shaped network layout requires a route through the inner city. A recent proposal called "Verkehr4.0" aims to extend the layout of the existing infrastructure by adding new express bus lines to connect outer city districts. This research paper investigates the direct traffic flow between the outer districts Stadtbergen and Göggingen in contrast to the existing flow via the central hub "Königsplatz". We implement an agent-based simulation comparing waiting times, travel times and total times spent on trips in the two scenarios. Furthermore, we model a measure dubbed "happiness" of the people as well as their willingness to change their mode of transport. The preliminary results of our simulation show that waiting time for public transport users decreases, while total time, travel time and happiness reveal no statistical difference through the introduction of an express bus line.
Geodemographic classifications are an important tool to support public-service decision making. While people are the focal point of geodemographics, classifications are often built on variables that describe populations rather than individuals. Synthetic populations, model-based approximations of the individual makeup of small census areas, remain largely unused for geodemographic classification, yet they can provide a more direct and holistic understanding of localized resource needs than existing approaches. This paper develops a new method for performing individual-centered geodemographic classifications using synthetic populations. The building blocks of this approach are abstractions of the synthetic population attributed to each small census area via affinity matrices computed from similarities in both the size and attributes among individuals. Using a rank-1 spectral decomposition of an area’s affinity matrix enables rapid computation of a dissimilarity metric which is compatible with cluster analysis techniques used in traditional geodemographic classifications. Using data from the American Community Survey (ACS), an example classification is developed for the Knoxville, TN, USA Public-Use Microdata Area (PUMA) to illustrate how distinctions can be drawn among small census areas in terms of specific types of representative individuals, providing a more tailored view of the groups that serve to benefit from spatial policy interventions. Beyond improving traditional public-domain geodemographic classifications, this approach provides a novel open-source alternative to commercial neighborhood segmentation products with added flexibility for custom research applications.
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.
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.
Many classic exploratory data analysis tools in quantitative geography, designed to measure global and local spatial autocorrelation (e.g. Moran’s I statistic), have become standard in modern GIS software. However, there has been little development in amending these tools for visualization and analysis of patterns captured in spatiotemporal data. We design and implement a new open-source Python library, VASA, that simplifies analytical pipelines in assessing spatiotemporal structure of data and enables enhanced visual display of the patterns. Using daily county-level social distancing metrics during 2020 obtained from two different sources (SafeGraph and Cuebiq), we demonstrate the functionality of the developed tool for a swift exploratory spatial data analysis and comparison of trends over larger administrative units.
