Center for Spatial Studies

This report documents the education, research, and outreach activities of the Center for the period March 2013 - March 2016. In addition, it presents a vision for the center based on plans set forth for the next 3 years.

This brochure seeks to broaden public understanding for the importance of spatial literacy for solving problems in science and for society.

This report summarizes discussions among ten university instructors from six disciplines in a symposium hosted by Diana Sinton at the University of Redlands in June 2008. The meeting was developed as a cooperative effort with the UC Santa Barbara Center for Spatial Studies.

The intent of the symposium was to review opportunies and challenges for establishing an undergraduate general course on spatial thinking.

This event provided a foundation for the establishment of a website devoted to instruction about fundamental spatial concepts (see http://teachspatial.org) and to the establishment of academic minors in spatial studies at UC Santa Barbara and the University of Redlands.

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.

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.

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 can be recommended as the safest approach in comparison to standard and roundabout layouts. Yet, space requirements may restrict in-town realization of this design.

The newsletter features (1) an article by Karl Grossner about the website http://teachspatial.org for discovering lesson materials for instruction in fundamental spatial concepts and (2) an essay  by Michael Goodchild on the spatially explicit and  peripatetic nature of (his) academic life.

This is the first issue of Vertices, areport on the activities of the spatial studies center known as spatial@ucsb. It features details about inauguration events centered on the theme of "Connecting our Region through GIS and Geospatial Technologies."

Two articles are featured. The first, by Wenwen Li, describes some of the mapping issues associated with a UC-Muliti-campus Research Program and Initiatives (MRPI) project about the metropolitan economy of the Los Angeles region. The second, by Andrea Nuernberger, focuses on knowledge assessment and spatial abilities of individuals with Alzheimer's disease, a project that she and Dan Montello inherited from the late Reginald Golledge, a pioneer in the study of human spatial behavior.

Cryptocurrencies and their underlying technologies such as blockchains and smart contracts are rapidly gaining traction in sectors such as banking, identity management, supply chain management, cloud-computing, voting, forecasting, and so forth. With this change in visibility and first signs of mainstream adoption, there is a growing interest in understanding the cryptocurrency ecosystem, e.g., regarding market trends or inherent risks. Interestingly, however, spatial and platial aspects have not yet received much attention. One possible reason for this lack of analysis may be due to the perception of cryptocurrencies being global and living outside of legal frameworks. We will show that this is a misconception and that understanding the cryptocurrency ecosystem requires looking at the spaces and places involved in their creation, consumption, and regulation.

The Spatial Search specialist meeting in Santa Barbara (December 2014) brought together 35 academic and industry representatives from computational, geospatial, and cognitive sciences with interest in focused discussions on the development of an interdisciplinary research agenda to advance spatial search from scientific and engineering viewpoints. The position papers from participants represent the shared expertise that guided discussions and the formulation of research questions about processes of spatial search and about the conceptual ideas, infrasturctures, and tools needed to enhance the search experience in both physical and virtual spaces. The call for position papers is included.

This flier provides a brief description of a workshop hosted in Santa Barbara on volunteered geographic information (VGI). Discussions focused on citizen motivations to provide information in the public domain, methods to validate such information, and ideas about the potentials and limitations to VGI. More than 40 position papers on these challenges are presented.

In advance of the meeting, 34 researchers prepared position statements on the following kinds of questions:

• What is the current state of knowledge with respect to spatiotemporal constraints on social networks and informationflows, particularly from a meta-network perspective?

• How can theories of social network interaction be extended to incorporate the constraining effects of space, time, theInternet, and mass media?

• Can probability distributions be developed for networks or network metrics that are parameterized by spatial andtemporal separation?

• What rich sources of data can be found to calibrate and parameterize these new models?

• What new metrics and models can be developed for assessing critical nodes, groups, and trails in and throughnetworks that take spatio-temporal constraints, the Internet, and mass-media effects into account?

• Can we develop novel methods for visualizing the operation of spatio-temporal constraints and their effects on the flow of ideas and information through meta-networks?

• What methods of inference are appropriate for detection of spatio-temporal and network constraints in crowd-sourced data, and what are appropriate metrics of uncertainty?

Image captioning and visual question answering are exciting problems that combine natural language processing and computer vision, currently attracting a significant interest. Some previous efforts have looked into these problems in the context of remote sensing imagery, opening a wide range of possibilities in terms of human interaction with these data through natural language. Still, the components that are involved in previously proposed models can be significantly improved, and evaluation has also mostly been carried out on relatively small datasets, often built automatically and without much diversity. This vision paper briefly surveys the current state-of-the-art in vision and language methods dealing with remote sensing data, also discussing some of the open challenges and possibilities for future work.

Where do people go when they have nowhere to be? Nonobligate activities are a significant part of our social and cultural lives, but there are no existing large scale data which characterize spatial variability in population allocation for these activities. As large scale population estimates have ever-finer resolutions, gaps in our ability to estimate this population segment have an increasingly large impact on high resolution population estimates. In this paper, we demonstrate an improved method for estimating the spatial allocation of the non-obligate population - people who are not at work, school, or in another residential institution. This method builds upon on anonymized and aggregate data on visits to public places, allocating the non-obligate population proportionally to worker population while accounting for the estimated ratio of visitors to workers in public places.

This paper envisions creating more inclusive communities through accessible urban places for not only those who identify as disabled but all equity-deserving groups. Concentrating on the street scale of the urban places, we propose identifying street scale accessibility features, and then, with the help of spatial data science and geospatial artificial intelligence, collecting and analyzing reliable data on these features to assess the accessibility of the urban places for movement diversity.

Acceleration of urbanisation is posing great challenges to sustainable development. Growing accessibility to big data and artificial intelligence (AI) technologies have revolutionised many fields and offered great potential for addressing pressing urban problems. However, using these technologies without explicitly considering responsibilities would bring new societal and environmental issues. To maximise the benefits of big data and AI while minimising potential issues, we envisage a conceptual framework of Responsible Urban Intelligence (RUI) and advocate an agenda for action. We first define RUI as consisting of three major components including urban problems, enabling technologies, and responsibilities; then introduce transparency, fairness, and eco-friendliness as the three dimensions of responsibilities which naturally link with the human, space, and time dimensions of cities; and further develop a four-stage implementation framework for responsibilities as consisting of solution design, data preparation, model building, and practical application; and finally present a research agenda for RUI addressing challenging issues including data and model transparency, tension between performance and fairness, and solving urban problems in an eco-friendly manner.

While a model prediction is a probabilistic claim about a system state to transpire in the future, a model projection is an if-then statement about the potential future of a system, by definition subject to (changes in) boundary conditions with an unknown likelihood. Despite a robust body of literature on the various potential purposes of models - and to predict is only one of these purposes - some modellers tend to refer to all their model outputs as predictions, while they are more often projections or neither of these two. Both geosimulation and spatial machine learning scholars are careless in how they refer to their model outputs. This is confusing for all involved and especially for the general public, for whom the model output is usually the only model component they get to see. In this paper we provide definitions, justifications, and a decision tree for classifying model outputs. This can help the GIScience community to gain clarity about what their model output entails.

Urban greenness is critical in evaluating the urban environmentand living conditions, significantly affecting human well-being and house prices. Unfortunately, satellite imagery from a bird-eye view does not fully capture urban greenness from a human-centered perspective, while human-perceived greenness from street-view images heavily relies on road networks and vehicle accessibility. In recent years, scholars started to explore greenness measurements from a simulative perspective, among which the simulation of the Viewshed Greenness Visibility Index (VGVI) received wide attention. However, the simulated VGVI lacks a comprehensive assessment. To fill this gap, we designed a field experiment in Fayetteville, Arkansas, by collecting 360-degree panoramas in different local climate zones. Further, we segmented these panoramas via the state-of-the-art DeeplabV2 neural network to obtain the Panoramic Greenness Visibility Index (PGVI), which served as the ground-truthing human-perceived greenness. We assessed the performance of VGVI by comparing it with PGVI calculated from field-collected panoramas. The results showed that, despite the disparity of performance in different local climate zones, VGVI significantly correlates to the PGVI, indicating its great potential for various domains that favor urban human-perceived greenness exposure.

This video was recorded live at the annual Spatial Lightning Talks on February 27, 2013 at the Mosher Alumni House, UC Santa Barbara. Each speaker was allotted three minutes to present a topic related to space —geographic or otherwise.

This video was recorded live at the annual Spatial Lightning Talks on February 27, 2013 at the Mosher Alumni House, UC Santa Barbara. Each speaker was allotted three minutes to present a topic related to space —geographic or otherwise.

This video was recorded live at the annual Spatial Lightning Talks on February 25, 2015 at the Mosher Alumni House, UC Santa Barbara. Each speaker was allotted three minutes to present a topic related to space—geographic or otherwise.