http://www.rssboard.org/rss-specification 720 https://escholarship.org/uc/spatial_ucsb_gisci/rss Recent eScholarship items from GIScience 2021 Short Paper Proceedings Wed, 2 Sep 2026 10:47:57 +0000 https://escholarship.org/uc/item/8t51k45t 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. https://escholarship.org/uc/item/8t51k45t Fri, 24 Sep 2021 00:00:00 +0000 Fu, Cheng Nanni, Mirco Yeghikyan, Gevorg Weibel, Robert https://escholarship.org/uc/item/9vv6j0m9 While eXplainable Artificial Intelligence (XAI) has significant potential to glassbox Deep Learning, there are challenges in applying it in the domain of Geospatial Artificial Intelligence (GeoAI). A land use case study highlights these challenges, which include the difficulty of selecting reference data/models, the shortcomings of gradients to serve as explanation, the limited semantics and knowledge scope in the explanation process of GeoAI, and underlying GeoAI processes that are not amenable to XAI. We conclude with possibilities to achieve Geographical XAI (GeoXAI). https://escholarship.org/uc/item/9vv6j0m9 Thu, 23 Sep 2021 00:00:00 +0000 Xing, Jin Sieber, Renee https://escholarship.org/uc/item/9pc4j56s Racial geography in US urban areas is extensively studied with the emphasis on assessing the extent of racial segregation. However, the used methodology has not changed for at least two decades; it relies on calculating ratios of population counts in the entire city and its subdivisions – census aggregation areas. This has a number of limitations; the two most important are: assessment of segregation depends on the subdivisions used, segregation can only be calculated for regions with census subdivisions. Here we present a different conceptualization of racial geography, which leads to a new method called racial landscape (RL). We use block-level census data to construct a high-resolution grid where each cell represents single race inhabitants. The result is a spatial, racial pattern; a degree of spatial autocorrelation of this pattern is a measure of segregation that does not require using subdivisions. We shortly describe the RL method and its application to Cook County, IL.... https://escholarship.org/uc/item/9pc4j56s Thu, 23 Sep 2021 00:00:00 +0000 Dmowska, Anna Stepinski, Tomasz Nowosad, Jakub https://escholarship.org/uc/item/9cs309kd Designing a Geospatial Question Answering (GeoQA) system that takes a user’s GIS-related domain question, understands how to gather the required data, how to analyse it, and how to present the results in a suitable format is arguably among the most important “moonshots” in the GeoAI field. In this study, we focus specifically on answering geo-event questions. This work begins by presenting a prototype process for generating workflows to answer geo-event questions by providing annotations of the domain, comprising a tool taxonomy we created from descriptions of geo-operations, a data type ontology obtained from the Core Concept Data types (CCD) ontology, and the annotations of the mentioned geo-operations with respect to the input/output pairs. Finally, the generated workflows are post-processed to restrict the solution space and provide more structured solutions. The results of this research provide a step towards the implementation of a geo-event QA system capable of answering... https://escholarship.org/uc/item/9cs309kd Thu, 23 Sep 2021 00:00:00 +0000 Kazemi Beydokhti, Mohammad Duckham, Matt Griffin, Amy Kasalica, Vedran https://escholarship.org/uc/item/8kg664zg Geographically weighted Poisson regression (GWPR) is widely used for spatial regression analysis of count data. However, it tends to be unstable because of a fundamental drawback of Poisson regression. To overcome the drawback, we introduce a log-linear approximation to estimate GWPR without relying on Poisson regression framework. The proposed approach approximates GWPR using the basic GWR modeling with transformed explained variables. Monte Carlo experiments show that the proposed GWPR outperforms the conventional GWPR in terms of both estimation accuracy and computationally efficiency. Finally, the proposed GWPR is applied to an analysis of coronavirus disease 2019 (COVID-19). https://escholarship.org/uc/item/8kg664zg Thu, 23 Sep 2021 00:00:00 +0000 Murakami, Daisuke Tsutsumida, Narumasa Yoshida, Takahiro Nakaya, Tomoki Lu, Binbin Harris, Paul https://escholarship.org/uc/item/8dc7t93b Engagement with natural areas has increased during the Covid-19 pandemic, and this may well form one of the enduring legacies of this time. A better understanding of human interactions with urban greenspace, and how patterns of use have changed, including inequalities of use, will be crucial for decision makers to adequately manage and direct resources within these natural spaces as we recover from the pandemic. Current evidence on use of natural spaces is limited and does not easily support site-specific analysis or with fine spatio-temporal distinctions. Coupled with difficulties on primary data gathered throughout the pandemic, there is a general knowledge gap on how changing behaviour has reshaped the use of natural areas and what inequalities have arisen in this dynamic. Through the case study of Glasgow’s open spaces, with a specific focus on one urban park, we apply new forms of urban big data from mobile devices to show how the use of greenspace has changed through the... https://escholarship.org/uc/item/8dc7t93b Thu, 23 Sep 2021 00:00:00 +0000 Sinclair, Michael Zhao, Qunshan Bailey, Nick Maadi, Saeed Hong, Jinhyun https://escholarship.org/uc/item/89h883x4 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... https://escholarship.org/uc/item/89h883x4 Thu, 23 Sep 2021 00:00:00 +0000 Lanini-Maggi, Sara Ruginski, Ian Tanner Fabrikant, Sara Irina https://escholarship.org/uc/item/88c5p28w 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. https://escholarship.org/uc/item/88c5p28w Thu, 23 Sep 2021 00:00:00 +0000 Kaiping, Gereon https://escholarship.org/uc/item/7km7x3w1 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... https://escholarship.org/uc/item/7km7x3w1 Thu, 23 Sep 2021 00:00:00 +0000 Kapaj, Armand Lanini-Maggi, Sara Fabrikant, Sara Irina https://escholarship.org/uc/item/6tt8j58m For human-centered mobile navigation systems, a computational landmark selection model is critical to automatically include landmarks for communicating routes with users. Although some empirical studies have shown that landmarks selected by familiar and unfamiliar wayfinders, respectively, differ significantly, existing computational models are solely focused on unfamiliar users and ignore selecting landmarks for familiar users, particularly in indoor environments. Meanwhile, it is unclear how the importance of salience metrics employed by machine learning approaches differs from that reported by human participants during landmark selection. In this study, we propose a LambdaMART-based ranking approach to computationally modelling indoor landmark selection. Two models, one for familiar and one for unfamiliar users, respectively, were trained from the human-labelled indoor landmark selection data. The importance of different salience measures in each model was then ranked and... https://escholarship.org/uc/item/6tt8j58m Thu, 23 Sep 2021 00:00:00 +0000 Zhou, Zhiyong Weibel, Robert Huang, Haosheng https://escholarship.org/uc/item/6q03b36x 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. https://escholarship.org/uc/item/6q03b36x Thu, 23 Sep 2021 00:00:00 +0000 Nowosad, Jakub Stepinski, Tomasz https://escholarship.org/uc/item/65t7h04k Spatial user-generated content (UGC) is increasingly being used to study a variety of geographical phenomena, including urban change in social and economic dimensions. Wikipedia content evolves over time and includes articles about geographical areas, points of interest, and geo-located events. In this article, we explore the spatio-temporal variability of geo-located Wikipedia pages, considering their complete editing history. Selecting Greater London as a case study, we study the association between Wikipedia activity and the socio-demographic characteristics of the spatial context. Editing activity grows rapidly at first, and is then followed by a slowdown, reaching a stable rate, with occasional spikes. The initial growth is distributed throughout the study area, but activity becomes gradually more concentrated in central areas. The socio-demographic variability is strongly related to the presence of Wikipedia pages, but only partially to the editing. This approach may support... https://escholarship.org/uc/item/65t7h04k Thu, 23 Sep 2021 00:00:00 +0000 Nawfee, Shahreen Muntaha Ballatore, Andrea De Sabbata, Stefano Tate, Nicholas https://escholarship.org/uc/item/62s7n79k This study builds a bridge between the literatures for geographically weighted regression (GWR) and compositional data analysis (CoDA). GWR allows the modeling of spatial heterogeneity in regression models and is increasingly used in various fields. CoDA provides unique and useful tools for compositional data, which are restricted by a constant-sum constraint. Although compositional data are common in many scientific areas, it is not until recently that increasingly sophisticated statistical methods have been deeply investigated. Many types of spatial models based on geostatistics, spatial statistics, and spatial econometrics for compositional data have been proposed. However, there is less attention to both spatial heterogeneity and the constant-sum constraint. In this study, we propose a GWR model for compositional data. This allows us to model spatially varying relationships while considering the constant-sum constraint. We applied this model to analyze household income compositions... https://escholarship.org/uc/item/62s7n79k Thu, 23 Sep 2021 00:00:00 +0000 Yoshida, Takahiro Murakami, Daisuke Seya, Hajime Tsutsumida, Narumasa Nakaya, Tomoki https://escholarship.org/uc/item/60v7597c The inadequacy of spatially explicit and accessible data portals continues to be a substantial barrier for policymakers and concerned authorities in the least developed countries. The purpose of this study is to determine the potentiality of night-time light (NTL) data to measure spatial road infrastructure development. The Day-Night Band (DNB) NTL data from the Visible Infrared Imaging Radiometer Suite (VIIRS) as well as Google Maps highways road data (RD) were used in this research. In order to analyze the correlation between VIIRS NTL and RD for two least developed countries, we performed the Chi-square test of independence, which revealed that the variables are dependent on one another. Following that, we computed the Cramer’s V test as a correlation coefficient to determine the strength of the association for both countries. Our findings revealed a correlation value of 0.334 in Bangladesh and a correlation value of 0.299 in Rwanda, demonstrating that VIIRS NTL and RD are... https://escholarship.org/uc/item/60v7597c Thu, 23 Sep 2021 00:00:00 +0000 Das, Satabdi Ahmed, Sharmin Haque, Summit Ismail, Sabir https://escholarship.org/uc/item/5zt0p1ft <p>In the city of Hanoi, Vietnam, as with other rapidly-developing cities, transport infrastructure is failing to keep pace with the burgeoning population. This has lead to high levels of congestion, air pollution, and a broad inequity in the accessibility of large parts of the city to residents. The emerging discipline of Urban Data Science has a valuable role in providing policy makers with robust evidence on which to base policy, but the discipline faces problems with the application of techniques that are based on assumptions that do not hold when applied to emerging economies.</p><p>This paper presents the preliminary outputs of a new programme of urban data science work that is being developed specifically for Hanoi. It leverages a spatial microsimulation approach to up-sample a bespoke travel survey and create a synthetic representation of the transport preferences of all residents in the city. These new data are used to assess the impacts that changes in the broader socio-economic... https://escholarship.org/uc/item/5zt0p1ft Thu, 23 Sep 2021 00:00:00 +0000 Malleson, Nick Nguyen Thi Thuy, Hang Bui Quang, Thanh Kieu, Minh Hoang Huu, Phe Comber, Alexis https://escholarship.org/uc/item/5dj756b5 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... https://escholarship.org/uc/item/5dj756b5 Thu, 23 Sep 2021 00:00:00 +0000 Rech, Eduard Timpf, Sabine https://escholarship.org/uc/item/59t385np The urban real estate market is shaped by spatially varying environmental and social determinants, such as the valuation of green spaces, proximity to transport, and distance to central business districts. Among all the spatially varying relationships between prices and housing characteristics, some tend to vary at a global spatial scale, whereas others vary at a local spatial scale. This study applies a random model to specify multi-scale spatial heterogeneity in the rental housing market by utilizing residential rent data in the Tokyo metropolitan area from 2017. The results show that spatially varying determinants impact rental housing prices at the global, moderate, and local scales. Further, we find that the estimation is flexible because the random model determines the spatial scale of each regression coefficient. https://escholarship.org/uc/item/59t385np Thu, 23 Sep 2021 00:00:00 +0000 Peng, Zhan Inoue, Ryo https://escholarship.org/uc/item/5016t2k9 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. https://escholarship.org/uc/item/5016t2k9 Thu, 23 Sep 2021 00:00:00 +0000 Noi, Evgeny Rudolph, Alexander Dodge, Somayeh https://escholarship.org/uc/item/4xj1008p 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... https://escholarship.org/uc/item/4xj1008p Thu, 23 Sep 2021 00:00:00 +0000 Tuccillo, Joseph https://escholarship.org/uc/item/4n31h85w 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... https://escholarship.org/uc/item/4n31h85w Thu, 23 Sep 2021 00:00:00 +0000 Stepinski, Tomasz Dmowska, Anna https://escholarship.org/uc/item/4c09g6wt 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 <i>k</i>-anonymous clusters, meaning that each cluster contains at least <i>k</i> locations. We assume that the locations are given as a set <i>T</i> ⊆ <i>V</i> of terminals in a weighted graph <i>G</i> = (<i>V</i>, <i>E</i>) representing a road network. Our approach is to compute a forest in <i>G</i>, i.e., a set of trees, each of which corresponds to a cluster. We ensure the <i>k</i>-anonymity of the clusters by constraining the trees to span at leastterminals each (plus an arbitrary number of non-terminal nodes... https://escholarship.org/uc/item/4c09g6wt Thu, 23 Sep 2021 00:00:00 +0000 Haunert, Jan-Henrik Schmidt, Daniel Schmidt, Melanie https://escholarship.org/uc/item/4bs0z3mc 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. https://escholarship.org/uc/item/4bs0z3mc Thu, 23 Sep 2021 00:00:00 +0000 Baranowski, Marek Gotlib, Dariusz Kozak, Jacek Zwoliński, Zbigniew https://escholarship.org/uc/item/4bp4q4z3 We identify automated landmark salience assessment in indoor environments as a problem related to pedestrian navigation systems that has not yet received much attention but is nevertheless of practical relevance. We therefore evaluate an approach based on visual information using images to capture the landmarks’ outward appearance. In this context we introduce the largest landmark image and salience value data set in the domain so far. We train various classifiers on domain agnostic visual features to predict the salience of landmarks. As a result, we are able to clarify the role of visual object features regarding perception of landmarks. Our results demonstrate that visual information has only limited expressiveness with respect to salience. https://escholarship.org/uc/item/4bp4q4z3 Thu, 23 Sep 2021 00:00:00 +0000 Donabauer, Gregor Ludwig, Bernd https://escholarship.org/uc/item/4575267v 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 &amp; greenhouse gases) on the route calculation - so that cars/bikes may receive real-time recommendations to avoid polluted areas. https://escholarship.org/uc/item/4575267v Thu, 23 Sep 2021 00:00:00 +0000 Scholz, Johannes Url, Christoph https://escholarship.org/uc/item/41t46420 This paper describes the novel development and application of a multi-scale geographically weighted discriminant analysis (MSGWDA). This is applied to a case study of survey data of attitudes to a proposed motorbike / scooter ban in Han Noi, Vietnam. It uses discriminant analysis to examine attitudes to the ban in relation to travel purposes, distances, respondent age and so on. The main part of the paper focuses on describing the novel MSGWDA approach, and the results indicate the varying scales of relationship between the different input variables and the categorical responses variable. The paper also reflects on the pervasive logic of the approaches used to fit multiscale geographically weighted bandwidths (for example in regression). These have historically been based on the iterative back-fitting approaches used in GAMs, but risk missing potentially important variable interactions amongst un-evaluated bandwidths because of the sequence of their application. It is argued... https://escholarship.org/uc/item/41t46420 Thu, 23 Sep 2021 00:00:00 +0000 Comber, Alexis Malleson, Nick Nguyen Thi Thuy, Hang Bui Quang, Thanh Kieu, Minh Huu Phe, Hoang Harris, Paul https://escholarship.org/uc/item/3wz9104b 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. https://escholarship.org/uc/item/3wz9104b Thu, 23 Sep 2021 00:00:00 +0000 Peek, Amber Martin, Michael Kolston, Sophie https://escholarship.org/uc/item/3376341d In this paper, we present the algorithm aimed to segment the type of geographical space where points are a substantial component. The research problem falls within the mainstream of Automated Unit Design (AUD). The objective function of the solution is a balance between the size of segmented units expressed as an attribute of points datasets and its agreement with constraints provided by the geographic space. An algorithm has three free parameters; two of them allow one to control the objective function: the size of segmented units and allowable deviation from the size. The paper contains a case study where we show how our approach segment the geographic space of the City of Poznań. https://escholarship.org/uc/item/3376341d Thu, 23 Sep 2021 00:00:00 +0000 Ośko, Mateusz Jasiewicz, Jarosław Szarwark, Przemysław https://escholarship.org/uc/item/1690j3zc The use of machine learning models (ML) in spatial statistics and urban analytics is increasing. However, research studying the generalisability of ML models from a geographical perspective had been sparse, specifically on whether a model trained in one context can be used in another. The aim of this research is to explore the extent to which standard models such as convolutional neural networks being applied on urban images can generalise across different geographies, through two tasks. First, on the classification of street frontages and second, on the prediction of real estate values. In particular, we find in both experiments that the models do not generalise well. More interestingly, there are also differences in terms of generalisability within the first case study which needs further exploration. To summarise, our results suggest that in urban analytics there is a need to systematically test out-of-geography results for this type of geographical image-based models. https://escholarship.org/uc/item/1690j3zc Thu, 23 Sep 2021 00:00:00 +0000 Law, Stephen Jeszenszky, Peter Yano, Keiji https://escholarship.org/uc/item/0x82c21d Increasing in-town bicycle traffic creates a demand for safe and efficient transportation infrastructure. A significant safety aspect is crossroad layout. Existing solutions such as protected crossroads, roundabouts and standard four-way crossings are investigated in terms of viewing angles between traffic participants. An agent-based simulation helps to generate data, which is further analysed. Special attention is paid to blind spots of vehicles during turns, overall line of sight and human field of view. We can show that especially protected crossroad designs have major advantages. Standard layouts convince in terms of the analysed field of view and possible blind spots. However, they demand extensive shoulder views and head turning especially during right turns. This makes them less safe. Roundabouts show medium results. Exiting this structure always requires a right turn which is, in terms of visibility, the most dangerous action for bicycles. We conclude that protected crossroads... https://escholarship.org/uc/item/0x82c21d Thu, 23 Sep 2021 00:00:00 +0000 Franke, Vincent Timpf, Sabine https://escholarship.org/uc/item/0kd9q103 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. https://escholarship.org/uc/item/0kd9q103 Thu, 23 Sep 2021 00:00:00 +0000 Thakur, Gautam Fan, Junchuan https://escholarship.org/uc/item/0kb4z5hq 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. https://escholarship.org/uc/item/0kb4z5hq Thu, 23 Sep 2021 00:00:00 +0000 Wallgrün, Jan Oliver Huang, Jiawei Zhao, Jiayan Brede, Benjamin Lau, Alvaro Klippel, Alexander