Assessing the placeness of locations through user-contributed content

Yuan, Xiaoyi; Crooks, Andrew

doi:10.1145/3356471.3365231

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…public interactive touch screens [18]) or taken with us on the go (smartphones, augmented reality), new methods for understanding and digitizing also spatial understanding are being constantly investigated. Location-based data mining from social media [9,25,26,52] now allows for large-scale data analysis. Crowdsourcing human experiences of places [8,14,24,51] can further provide authentic experiences of spaces in the urban scale.…”

Section: Spatial Experience In Hcimentioning

confidence: 99%

Investigating Human Scale Spatial Experience

Ville

Oppenlaender

Gonçalves

et al. 2021

Proc. ACM Hum.-Comput. Interact.

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Spatial experience, or how humans experience a given space, has been a pivotal topic especially in urban-scale environments. On the human scale, HCI researchers have mostly investigated personal meanings or aesthetic and embodied experiences. In this paper, we investigate the human scale as an ensemble of individual spatial features. Through large-scale online questionnaires we first collected a rich set of spatial features that people generally use to characterize their surroundings. Second, we conducted a set of field interviews to develop a more nuanced understanding of the feature identified as most important: perceived safety. Our combined quantitative and qualitative analysis contributes to spatial understanding as a form of context information and presents a timely investigation into the perceived safety of human scale spaces. By connecting our results to the broader scientific literature, we contribute to the field of HCI spatial understanding.

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Section: Spatial Experience In Hcimentioning

confidence: 99%

Investigating Human Scale Spatial Experience

Ville

Oppenlaender

Gonçalves

et al. 2021

Proc. ACM Hum.-Comput. Interact.

View full text Add to dashboard Cite

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GeoAI at ACM SIGSPATIAL

Gao²,

Lunga

et al. 2019

SIGSPATIAL Special

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Geospatial artificial intelligence (GeoAI) is an interdisciplinary field that has received tremendous attention from both academia and industry in recent years. This article reviews the series of GeoAI workshops held at the Association for Computing Machinery (ACM) International Conference on Advances in Geographic Information Systems (SIGSPATIAL) since 2017. These workshops have provided researchers a forum to present GeoAI advances covering a wide range of topics, such as geospatial image processing, transportation modeling, public health, and digital humanities. We provide a summary of these topics and the research articles presented at the 2017, 2018, and 2019 GeoAI workshops. We conclude with a list of open research directions for this rapidly advancing field.

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GeoAI at ACM SIGSPATIAL

Lunga

Newsam

et al. 2021

SIGSPATIAL Special

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Geospatial Artificial Intelligence (GeoAI) is an interdisciplinary field enjoying tremendous adoption. However, the efficient design and implementation of GeoAI systems face many open challenges. This is mainly due to the lack of non-standardized approaches to artificial intelligence tool development, inadequate platforms, and a lack of multidisciplinary engagements, which all motivate domain experts to seek a shared stage with scientists and engineers to solve problems of significant impact on society. Since its inception in 2017, the GeoAI series of workshops has been co-located with the Association for Computing Machinery International Conference on Advances in Geographic Information Systems. The workshop series has fostered a nexus for geoscientists, computer scientists, engineers, entrepreneurs, and decision-makers, from academia, industry, and government to engage in artificial intelligence, spatio-temporal data computing, and geospatial data science research, motivated by various challenges. In this article, we revisit and discuss the state of GeoAI open research directions, the recent developments, and an emerging agenda calling for a continued cross-disciplinary community engagement.

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Assessing the placeness of locations through user-contributed content

Cited by 4 publications

References 32 publications

Investigating Human Scale Spatial Experience

Investigating Human Scale Spatial Experience

GeoAI at ACM SIGSPATIAL

GeoAI at ACM SIGSPATIAL

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