Recent spatial_ucsb items

Guide to the Geographic Approach Instructor Template for GIS Ethical Case Studies

Tue, 30 Sep 2025 00:00:00 +0000

Guide to the Geographic Approach Instructor Template for GIS Ethical Case Studies

Guide to the Geographic Approach Assessment Questions Template for Instructors

Tue, 30 Sep 2025 00:00:00 +0000

Guide to the Geographic Approach Assessment Questions Template for Instructors

Guide to the Geographic Approach Supplemental Teaching Materials Template for Instructors

Tue, 30 Sep 2025 00:00:00 +0000

Guide to the Geographic Approach Supplemental Teaching Materials Template for Instructors

Guide to the Geographic Approach Data Access Guide Template for Instructors

Tue, 30 Sep 2025 00:00:00 +0000

Guide to the Geographic Approach Data Access Guide Template for Instructors

Guide to the Geographic Approach Video Script Template for Instructors

Tue, 30 Sep 2025 00:00:00 +0000

Guide to the Geographic Approach Video Script Template for Instructors

Guide to the Geographic Approach Educator Introduction Template for Instructors

Tue, 30 Sep 2025 00:00:00 +0000

Guide to the Geographic Approach Educator Introduction Template for Instructors

Guide to the Geographic Approach GISCI Content Area Appendix for Instructors

Tue, 30 Sep 2025 00:00:00 +0000

Guide to the Geographic Approach GISCI Content Area Appendix for Instructors

Guide to the Geographic Approach Guide to Designing GIS Ethics Lessons and Case Studies

Tue, 30 Sep 2025 00:00:00 +0000

Guide to the Geographic Approach Guide to Designing GIS Ethics Lessons and Case Studies

Guide to the Geographic Approach Student Template for GIS Ethical Case Studies

Tue, 30 Sep 2025 00:00:00 +0000

Guide to the Geographic Approach Student Template for GIS Ethical Case Studies

Reproducing Spatial Data Science Publications

Thu, 31 Aug 2023 00:00:00 +0000

Reproducibility is one of the corner stones of science: when studies cannot be reproduced it is hard to convey that they contain new findings of general truth. We constrain ourselves here to computational aspects of spatial data science, and discuss the challenges posed by always evolving software, scientific software developer communities, upstream and downstream dependencies, the publishing industry, and report on experiences from developer communities, and look at convergence in the spatial data science software ecosystems.

DeepMCLP: Solving the MCLP with Deep Reinforcement Learning for Urban Spatial Computing

Wed, 23 Aug 2023 00:00:00 +0000

Maximal Covering Location Problem (MCLP) is a classical spatial optimization problem that plays a significant role in urban spatial computing. Due to its NP-hard, finding an exact solution for this problem is computationally challenging. This study proposes a deep reinforcement learning-based approach called DeepMCLP to address the MCLP problem. We model MCLP as a Markov Decision Process. The encoder with attention mechanisms learns the interaction between demand points and facility points and the decoder outputs a probability distribution over candidate facility points, and a greedy policy is employed to select facility points, resulting in a feasible solution. We utilize the trained DeepMCLP model to solve both artificially synthesized data and real-world scenarios. Experimental results demonstrate that our algorithm effectively solves the MCLP problem, achieving faster solving times compared to mature solvers and smaller optimality gaps compared to the genetic algorithm. Our...

A Semantic Model for Generic Terms and Place Nouns

Wed, 23 Aug 2023 00:00:00 +0000

This paper offers a model of the semantic content of spatial nouns as generic terms in place names (e.g. Square in Trafalgar Square) and as descriptors for places ("place nouns", e.g. street in the second street). The model is based on a variant of Frame Semantics in which different context- and community-based uses (e.g. general, daily uses; specialised uses; legal, normative uses) are modelled as as sets/matrices of attribute-value pairs, or frames. The attributes forming these frames are based on data extraction from corpora (general uses), Wikipedia articles (specialised uses), and professional geographical dictionary (legal uses) as contexts. It is shown that uses associated to each context define frames varying considerably in content; however, a semantic overlap relation connects these frames. Consequences for a general theory of the semantics of place and geonames are discussed.

ChatGPT as a mapping assistant: A novel method to enrich maps with generative AI and content derived from street-level photographs

Wed, 23 Aug 2023 00:00:00 +0000

This paper explores the concept of leveraging generative AI as a mapping assistant for enhancing the efficiency of collaborative mapping. We present the results of an experiment that combines multiple sources of volunteered geographic information (VGI) and large language models (LLMs). Three analysts described the content of crowdsourced Mapillary street-level photographs taken along roads in a small test area in Miami, Florida. GPT-3.5-turbo was instructed to suggest the most appropriate tagging for each road in OpenStreetMap (OSM). The study also explores the utilization of BLIP-2, a state-of-the-art multimodal pre-training method as an artificial analyst of street-level photographs in addition to human analysts. Results demonstrate two ways to effectively increase the accuracy of mapping suggestions without modifying the underlying AI models: by (1) providing a more detailed description of source photographs, and (2) combining prompt engineering with additional context (e.g....

4DModeller (fdmr): A Comprehensive R Package for Spatio-Temporal Modelling

Wed, 23 Aug 2023 00:00:00 +0000

Spatio-temporal data analysis is crucial in many research fields. However, modelling large-scale spatio-temporal data presents challenges such as high computational demands, complex correlation structures, and the separation of mixed sources. To address these issues, we are developing 4DModeller (fdmr), a robust and user-friendly R package designed to model spatio-temporal data within a Bayesian framework. The software package offers a comprehensive solution for visualizing, analyzing and modelling different types of spatio-temporal data in various disciplines. By incorporating Bayesian hierarchical models, "fdmr" allows for the flexible integration of prior knowledge and data uncertainty into the modelling process. By utilizing the Integrated Nested Laplace Approximations (INLA) algorithm and the stochastic partial differential equations (SPDE) method for model inference, "fdmr" significantly reduces the computational complexity of handling high-resolution, highdimensional spatio-temporal...

Using a narrative-based approach as a safeguard against bias related harm in algorithmic tools and services

Wed, 23 Aug 2023 00:00:00 +0000

The ubiquity of algorithmic tools and services (ATS) in spatial data science has led to increased concerns about the biases they carry. This vision paper explores the biases inherent in ATS, encompassing computational, statistical, human, and systemic biases, and those compounded by multinational corporations. It underscores the imperative to address these biases, advocating a narrative-based approach to counteract them and promote equitable outcomes. This approach not only heightens awareness of embedded biases but also charts a course toward their mitigation.

Assessing simulated visible greenness in urban environments

Wed, 23 Aug 2023 00:00:00 +0000

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...

Why the term prediction is overused

Wed, 23 Aug 2023 00:00:00 +0000

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.

Responsible Urban Intelligence: Towards a Research Agenda

Wed, 23 Aug 2023 00:00:00 +0000

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...

Context for Leisure Walking Routes: A Vision for a Spatial-Platial Approach

Wed, 24 Aug 2022 00:00:00 +0000

Providing recommendations for interesting and engaging leisure walking routes is a complex problem due to the subjective and personal nature of the activity. Existing work has often focused on recommending the quickest or most popular walks. However, these routes often lack detail on the contextual and experiential factors of walks and do not attempt to match the requirements with those of users. This article presents a vision of how more contextual detail can be applied to walking routes. We consider how existing analysis and spatial data mining techniques, including real-time clustering, viewshed analysis, and colocation patterns, could be used to extend a place-based understanding of leisure walking routes. By using spatial methods to extrapolate a rich platial understanding of the locations of a walk, the proposed methods in this article will support an emerging framework for curating engaging leisure walking experiences, recommending routes beyond those of the quickest or...

On the Role of Spatial Data Science for Federated Learning

Wed, 24 Aug 2022 00:00:00 +0000

Federated learning (FL) has the potential to mitigate privacy risks and communication costs associated with classical machine learning and data science approaches. Given the distributed nature of FL, many of its use cases face challenges related to spatiotemporal data, geographical analysis, and spatial statistics. However, so far, FL has received little attention by the GIScience community. In this paper, we provide a first overview of the key challenges in FL and how they relate to spatial data science. This paper thus aims to provide the basis for future contributions to federated learning practices by the (geo)spatial research community.

Geoparsing comments from Reddit to extract mental place connectivity within the United Kingdom

Wed, 24 Aug 2022 00:00:00 +0000

Place connectivity is explored between geographic locations extracted from comments on Reddit. Unlike formally structured geographic data, this corpus of unstructured text provides connections derived from co-occurring locations, capturing subconscious links between them, alongside inherent biases. Our work demonstrates the ability to link locations mentioned by unique users, building ‘mental’ place connections for over 50,000 unique locations in the United Kingdom. Sentiment regarding locations is compared against their levels of connectivity, demonstrating that user opinions regarding locations are likely drivers in mental place connectivity.

Operationalizing Spatial Causal Inference

Wed, 24 Aug 2022 00:00:00 +0000

Most spatial inquiries seek to investigate causal questions about spatial processes, but many quantitative spatial methods are designed to identify correlations and spatial patterns. Studying the structure of associations that make up a spatial pattern can provide information about what the process that generated that pattern is likely to be, but it does not provide a means of testing any one explanation against alternative explanations. Causal inference provides a set of approaches to formally make comparisons between explanations. An opportunity exists to incorporate these techniques and spatialize the theory of cause in GIScience by building on recent advances in computer science and statistics. However, implementing causal inference in geography may require a shift in the design of geographic information systems.

Extending the Conversation: A Vision for Urban Accessibility for Diverse Mobilities through GeoAI

Wed, 24 Aug 2022 00:00:00 +0000

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.

Improving the LandScan USA Non-Obligate Population Estimate (NOPE)

Wed, 24 Aug 2022 00:00:00 +0000

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.

Towards Natural Language Interfaces for Interacting with Remote Sensing Data

Wed, 24 Aug 2022 00:00:00 +0000

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.

Understanding the Spatial, Platial, and Temporal Properties of Cryptocurrency Ecosystems

Wed, 22 Jun 2022 00:00:00 +0000

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.

Parking Recommendation Service Using Deep Learning

Tue, 30 Nov 2021 00:00:00 +0000

Parking Recommendation Service Using Deep Learning

Visualizing environmental justice issues in urban areas with a community input approach

Tue, 30 Nov 2021 00:00:00 +0000

Visualizing environmental justice issues in urban areas with a community input approach

A Prototypical Geospatial Knowledge Graph And Spatio-Temporal Question Answering for Supply Chain Visibility

Tue, 30 Nov 2021 00:00:00 +0000

A Prototypical Geospatial Knowledge Graph And Spatio-Temporal Question Answering for Supply Chain Visibility

"Data Horror": Mapping (Spatial) Data Privacy Violations onto a Cognitive Account of Horror

Tue, 30 Nov 2021 00:00:00 +0000

"Data Horror": Mapping (Spatial) Data Privacy Violations onto a Cognitive Account of Horror

Spatial Linked Data Approach for Trace Data in Digital Humanities

Tue, 30 Nov 2021 00:00:00 +0000

Spatial Linked Data Approach for Trace Data in Digital Humanities

Data augmentation for spatial disease mapping

Tue, 30 Nov 2021 00:00:00 +0000

Data augmentation for spatial disease mapping

A Taxonomic Classification Approach for Global Spatio-temporal Data

Tue, 30 Nov 2021 00:00:00 +0000

A Taxonomic Classification Approach for Global Spatio-temporal Data

Hidden spatial clusters - and how to find them

Tue, 30 Nov 2021 00:00:00 +0000

Hidden spatial clusters - and how to find them

Sidewalk measurements from satellite images: Preliminary findings

Tue, 30 Nov 2021 00:00:00 +0000

Sidewalk measurements from satellite images: Preliminary findings

A Vision for Veridical Spatial Data Science

Tue, 30 Nov 2021 00:00:00 +0000

A Vision for Veridical Spatial Data Science

Classifying Narcotrafficking Spatial Event Documents using Transformers

Tue, 30 Nov 2021 00:00:00 +0000

Classifying Narcotrafficking Spatial Event Documents using Transformers

Deriving Spatio-Temporal Geographies of Human Mobility from GPS traces

Tue, 30 Nov 2021 00:00:00 +0000

Deriving Spatio-Temporal Geographies of Human Mobility from GPS traces

Measuring Polycentricity: A Whole Graph Embedding Perspective

Fri, 24 Sep 2021 00:00:00 +0000

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.

Integrating XAI and GeoAI