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Accurate citywide crowd activity prediction (CAP) can enable proactive crowd mobility management and timely responses to urban events, which has become increasingly important for a myriad of smart city planning and management purposes. However, complex correlations across the crowd activities, spatial and temporal urban environment features and theirinteractivedependencies, and relevant external factors (e.g., weather conditions) make it highly challenging to predict crowd activities accurately in terms of different venue categories (for instance, venues related to dining, services, and residence) and varying degrees (e.g., daytime and nighttime). To address the above concerns, we proposeSTICAP, a citywide spatio-temporal interactive crowd activity prediction approach. In particular,STICAPtakes in the location-based social network check-in data (e.g., from Foursquare/Gowalla) as the model inputs and forecasts the crowd activity within each time step for each venue category. Furthermore, we have integrated multiple levels of temporal discretization to interactively capture the relations with historical data. Then, three parallelResidual Spatial Attention Networks(RSAN) in theSpatial Attention Componentexploit the hourly, daily, and weekly spatial features of crowd activities, which are further fused and processed by theTemporal Attention Componentforinteractive CAP. Along with other external factors such as weather conditions and holidays,STICAPadaptively and accurately forecasts the final crowd activities per venue category, enabling potential activity recommendation and other smart city applications. Extensive experimental studies based on three different real-world crowd activity datasets have demonstrated that our proposedSTICAPoutperforms the baseline and state-of-the-art algorithms in CAP accuracy, with an average error reduction of 35.02%.
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A Hierarchical Approach for Geocoding Birthplaces in Temporally Continuous Crowd-Sourced Family Tree Data
Geocoding is a fundamental yet complex step in temporal studies due to constantly evolving administrative borders and place names, and the uncertainty of geographic and temporal information. For example, identifying locations within crowdsourced datasets, such as family trees, is complex because recorded place names may be uncertain, inaccurate, and contain varying spellings (for instance, full names or abbreviations) and in inconsistent formats, such as mentioning only the country, or state, or a combination of city, county, state, and country. Moreover, place names and administrative boundaries drastically change over time, adding another layer of complexity to the geocoding process of fine-scale places. This paper presents a workflow for geocoding birthplaces of US-born individuals from crowd-sourced genealogical files spanning from 1789 to 1940. We introduce a method that geocodes the birth locations at the finest possible level by matching places with corresponding historical administrative boundaries within a range of individuals’ birth years. Our preliminary study analyzing 72,335 trees with over 250 million individual records shows the potential of our approach for use in complex crowd-generated spatio-temporal datasets.
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- Award ID(s):
- 2215568
- PAR ID:
- 10514703
- Publisher / Repository:
- CAGIS+UCGIS Symposium 2024
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Seeking spatiotemporal patterns about how citizens interact with the urban space is critical for understanding how cities function. Such interactions were studied in various forms focusing on patterns of people’s presence, action, and transition in the urban environment, which are defined as human-urban interactions in this paper. Using human activity datasets that utilize mobile positioning technology for tracking the locations and movements of individuals, researchers developed stochastic models to uncover preferential return behaviors and recurrent transitional activity structures in human-urban interactions. Ad-hoc heuristics and spatial clustering methods were applied to derive meaningful activity places in those studies. However, the lack of semantic meaning in the recorded locations makes it difficult to examine the details about how people interact with different activity places. In this study, we utilized geographic context-aware Twitter data to investigate the spatiotemporal patterns of people’s interactions with their activity places in different urban settings. To test consistency of our findings, we used geo-located tweets to derive the activity places in Twitter users’ location histories over three major U.S. metropolitan areas: Greater Boston Area, Chicago, and San Diego, where the geographic context of each location was inferred from its closest land use parcel. The results showed striking spatial and temporal similarities in Twitter users’ interactions with their activity places among the three cities. By using entropy-based predictability measures, this study not only confirmed the preferential return behaviors as people tend to revisit a few highly frequented places but also revealed detailed characteristics of those activity places.more » « less
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