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.
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.
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 the most popular.
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.
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.
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.
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.
