GeoAI at ACM SIGSPATIAL

Hu, Yingjie; Gao, Song; Lunga, Dalton; Li, Wenwen; Newsam, Shawn; Bhaduri, Budhendra

doi:10.1145/3377000.3377002

Cited by 38 publications

(8 citation statements)

References 52 publications

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“…The outstanding performance of GWR can be attributed to its ability to explicitly model spatial autocorrelation, a local effect we have observed in neighborhood-level obesity prevalence during the analysis stage. While deep learning models have demonstrated outstanding performances in image recognition and natural language processing [64], their performance on tabular data (i.e., data structured into rows and columns, such as those used in this study) seems to be similar to statistical models and other "shallow learning" models such as random forest. Similar results have also been reported in the literature [72][73][74].…”

Section: Methodological Implicationsmentioning

confidence: 87%

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Deriving neighborhood-level diet and physical activity measurements from anonymized mobile phone location data for enhancing obesity estimation

Zhou

Tirabassi

et al. 2022

Int J Health Geogr

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Background Obesity is a serious public health problem. Existing research has shown a strong association between obesity and an individual’s diet and physical activity. If we extend such an association to the neighborhood level, information about the diet and physical activity of the residents of a neighborhood may improve the estimate of neighborhood-level obesity prevalence and help identify the neighborhoods that are more likely to suffer from obesity. However, it is challenging to measure neighborhood-level diet and physical activity through surveys and interviews, especially for a large geographic area. Methods We propose a method for deriving neighborhood-level diet and physical activity measurements from anonymized mobile phone location data, and examine the extent to which the derived measurements can enhance obesity estimation, in addition to the socioeconomic and demographic variables typically used in the literature. We conduct case studies in three different U.S. cities, which are New York City, Los Angeles, and Buffalo, using anonymized mobile phone location data from the company SafeGraph. We employ five different statistical and machine learning models to test the potential enhancement brought by the derived measurements for obesity estimation. Results We find that it is feasible to derive neighborhood-level diet and physical activity measurements from anonymized mobile phone location data. The derived measurements provide only a small enhancement for obesity estimation, compared with using a comprehensive set of socioeconomic and demographic variables. However, using these derived measurements alone can achieve a moderate accuracy for obesity estimation, and they may provide a stronger enhancement when comprehensive socioeconomic and demographic data are not available (e.g., in some developing countries). From a methodological perspective, spatially explicit models overall perform better than non-spatial models for neighborhood-level obesity estimation. Conclusions Our proposed method can be used for deriving neighborhood-level diet and physical activity measurements from anonymized mobile phone data. The derived measurements can enhance obesity estimation, and can be especially useful when comprehensive socioeconomic and demographic data are not available. In addition, these derived measurements can be used to study obesity-related health behaviors, such as visit frequency of neighborhood residents to fast-food restaurants, and to identify primary places contributing to obesity-related issues.

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Section: Methodological Implicationsmentioning

confidence: 87%

“…DNNs and other deep learning models have shown outstanding predictive power in recent years [63,64]. A DNN is made of multiple successive layers of neurons and can learn a complex nonlinear relation between the input features and the target variables.…”

Section: Deep Neural Networkmentioning

confidence: 99%

Deriving neighborhood-level diet and physical activity measurements from anonymized mobile phone location data for enhancing obesity estimation

Zhou

Tirabassi

et al. 2022

Int J Health Geogr

Self Cite

View full text Add to dashboard Cite

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“…Therefore, it has gained huge research attention in recent years and increasingly attracted many researchers to carry forward their research work in this domain. Besides, many academic conferences and workshops [7][8][9] have been conducted to gather the data related to informal texts. The Association for Computational Linguistics (ACL) [10] and North American Association for Computational Linguistics (NAACL) [11] have been encouraging the researchers and students to actively participate in their conferences and workshops to gain knowledge on both formal and informal text.…”

Section: Related Workmentioning

confidence: 99%

Effective Preprocessing and Normalization Techniques for COVID-19 Twitter Streams with POS Tagging via Lightweight Hidden Markov Model

Narayanasamy

Qaisar

et al. 2022

Journal of Sensors

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The major focus of this research work is to refine the basic preprocessing steps for the unstructured text content and retrieve the potential conceptual features for further enhancement processes such as semantic enrichment and named entity recognition. Although some of the preprocessing techniques such as text tokenization, normalization, and Part-of-Speech (POS) tagging work exceedingly well on formal text, it has not performed well when it is applied into informal text such as tweets and short messages. Hence, we have given the enhanced text normalization techniques to reduce the complexity persist over the twitter streams and eliminate the overfitting issues such as text anomalies and irregular boundaries while fixing the grammar of the text. The hidden Markov model (HMM) has been pervasively used to extract the core lexical features from the Twitter dataset and suitably adapt the external documents to supplement the extraction techniques to complement the tweet context. Using this Markov process, the POS tags are identified as states of the Markov process, and words are the desired results of the model. As this process is very crucial for the next stage of entity extraction and classification, the effective handling of informal text is considered to be important and therefore proposed the most effective hybrid approach to deal with the issues appropriately.

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The aim of this special issue in the journal of Geoinformatica is to bring together the latest research on the burgeoning topic of "Geospatial Artificial Intelligence (GeoAI)" at the intersection of geospatial studies and artificial intelligence (AI) technologies, especially spatially-explicit machine/deep learning methods and knowledge graphs [1,2,5]. GeoAI provides novel approaches for addressing a variety of problems in both our natural environment and human society.

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mentioning

confidence: 99%