Name
Abstract | ||
|---|---|---|
Socio-economic maps contain important information regarding the population of a country. Computing these maps is critical given that policy makers often times make important decisions based upon such information. However, the compilation of socio-economic maps requires extensive resources and becomes highly expensive. On the other hand, the ubiquitous presence of cell phones, is generating large amounts of spatio-temporal data that can reveal human behavioral traits related to specific socio-economic characteristics. Traditional inference approaches have taken advantage of these datasets to infer regional socio-economic characteristics. In this paper, we propose a novel approach whereby topic models are used to infer socio-economic levels from largescale spatio-temporal data. Instead of using a pre-determined set of features, we use latent Dirichlet Allocation (LDA) to extract latent recurring patterns of co-occurring behaviors across regions, which are then used in the prediction of socio-economic levels. We show that our approach improves state of the art prediction results by approximate to 9%. |
Year | Venue | Field |
|---|---|---|
2016 | THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE | Data science,Population,Latent Dirichlet allocation,Computer science,Inference,Natural disaster,Artificial intelligence,Topic model,Machine learning |
DocType | Citations | PageRank |
Conference | 2 | 0.37 |
References | Authors | |
15 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lingzi Hong | 1 | 18 | 3.35 |
| Enrique Frías-Martínez | 2 | 214 | 17.71 |
| Vanessa Frías-Martínez | 3 | 107 | 10.32 |