Spatial and Temporal Characterization of Activity in Public Space, 2019–2020

Brelsford, Christa; Moehl, Jessica; Weber, Eric; Sparks, Kevin; Tuccillo, Joseph V; Rose, Amy

doi:10.1038/s41597-022-01480-6

Cited by 16 publications

(7 citation statements)

References 7 publications

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“…Their main objective is to recognize simple activities performed by solitary individuals. In contrast, public spaces 13 , 14 offer a more complex scenario with a wider range of users and activities, including group interactions, posing greater challenges than those encountered in environments designed for individual use. This complexity in public settings has spurred considerable research interest 15 , 16 .…”

Section: Background and Summarymentioning

confidence: 99%

A dataset of ambient sensors in a meeting room for activity recognition

Kim,

Lee

et al. 2024

Sci Data

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As IoT technology advances, using machine learning to detect user activities emerges as a promising strategy for delivering a variety of smart services. It is essential to have access to high-quality data that also respects privacy concerns and data streams from ambient sensors in the surrounding environment meet this requirement. However, despite growing interest in research, there is a noticeable lack of datasets from ambient sensors designed for public spaces, as opposed to those for private settings. To bridge this gap, we design the DOO-RE dataset within an actual meeting room environment, equipped with three types of ambient sensors: those triggered by actuators, users, and the environment itself. This dataset is compiled from the activities of over twenty students throughout a period of four months. DOO-RE provides reliable and purpose-oriented activity data in a public setting, with activity labels verified by multiple annotators through a process of cross-validation to guarantee data integrity. DOO-RE categorizes nine different types of activities and facilitates the study of both single and group activities. We are optimistic that DOO-RE will play a significant role in advancing human activity recognition technologies, enhancing smart automation systems, and enabling the rapid setup of smart spaces through ambient sensors.

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Section: Background and Summarymentioning

confidence: 99%

A dataset of ambient sensors in a meeting room for activity recognition

Kim,

Lee

et al. 2024

Sci Data

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“…It records the number of visits paid to each POI by residents of each CBG, aggregated on a monthly basis, along with supplementary statistics such as popularity by week and by hour. Although this dataset is a sample of all happened activities, existing works (Kang et al 2020;Brelsford et al 2022) have validated its consistency with other datasets and representativeness of the real-world situation. To account for uneven sampling across CBGs, we follow the common practice (Chang et al 2021) to reweight POI visitations according to the ratio between the number of devices residing in each CBG to the corresponding CBG.…”

Section: Data Descriptionmentioning

confidence: 89%

Getting Back on Track: Understanding COVID-19 Impact on Urban Mobility and Segregation with Location Service Data

Lin

Hao

et al. 2023

ICWSM

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Understanding the impact of COVID-19 on urban life rhythms is crucial for accelerating the return-to-normal progress and envisioning more resilient and inclusive cities. While previous studies either depended on small-scale surveys or focused on the response to initial lockdowns, this paper uses large-scale location service data to systematically analyze the urban mobility behavior changes across three distinct phases of the pandemic, i.e., pre-pandemic, lockdown, and reopen. Our analyses reveal two typical patterns that govern the mobility behavior changes in most urban venues: daily life-centered urban venues go through smaller mobility drops during the lockdown and more rapid recovery after reopening, while work-centered urban venues suffer from more significant mobility drops that are likely to persist even after reopening. Such mobility behavior changes exert deeper impacts on the underlying social fabric, where the level of mobility reduction is positively correlated with the experienced segregation at that urban venue. Therefore, urban venues undergoing more mobility reduction are also more filled with people from homogeneous socio-demographic backgrounds. Moreover, mobility behavior changes display significant heterogeneity across geographical regions, which can be largely explained by the partisan inclination at the state level. Our study shows the vast potential of location service data in deriving a timely and comprehensive understanding of the social dynamic in urban space, which is valuable for informing the gradual transition back to the normal lifestyle in a “post-pandemic era”.

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“…We estimate the total number of customers by county using county population, customer totals from each utility, and the coverage area of each utility. County population is derived from LandScan USA ( https://landscan.ornl.gov/ ) using the average of daytime and nighttime population 20 , 21 , information on utility customer totals is collected from the Energy Information Administration’s form 861 (EIA-861) ( https://www.eia.gov/electricity/data/eia861/ ), and Electric Retail Service Territories geospatial coverage data is drawn from Homeland Infrastructure Foundation-Level Data (HIFLD) ( https://hifld-geoplatform.opendata.arcgis.com/ ).…”

Section: Methodsmentioning

confidence: 99%

A dataset of recorded electricity outages by United States county 2014–2022

Brelsford,

Tennille,

Myers

et al. 2024

Sci Data

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In this Data Descriptor, we present county-level electricity outage estimates at 15-minute intervals from 2014 to 2022. By 2022 92% of customers in the 50 US States, Washington DC, and Puerto Rico are represented. These data have been produced by the Environment for Analysis of Geo-Located Energy Information (EAGLE-ITM), a geographic information system and data visualization platform created at Oak Ridge National Laboratory to map the population experiencing electricity outages every 15 minutes at the county level. Although these data do not cover every US customer, they represent the most comprehensive outage information ever compiled for the United States. The rate of coverage increases through time between 2014 and 2022. We present a quantitative Data Quality Index for these data for the years 2018–2022 to demonstrate temporal changes in customer coverage rates by FEMA region and indicators of data collection gaps or other errors.

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Spatial and Temporal Characterization of Activity in Public Space, 2019–2020

Cited by 16 publications

References 7 publications

A dataset of ambient sensors in a meeting room for activity recognition

A dataset of ambient sensors in a meeting room for activity recognition

Getting Back on Track: Understanding COVID-19 Impact on Urban Mobility and Segregation with Location Service Data

A dataset of recorded electricity outages by United States county 2014–2022

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