Evaluating geo-located Twitter data as a control layer for areal interpolation of population |
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| Institution: | 1. Department of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou, Zhejiang 310027, PR China;2. Department of Geography, University of Connecticut, 215 Glenbrook Road, Storrs, CT 06269, USA;1. Research Network in Biodiversity and Evolutionary Biology, Research Centre in Biodiversity and Genetic Resources (InBIO-CIBIO), Universidade do Porto, Campus Agrário de Vairão, Rua Padre Armando Quintas, PT4485-661 Vairão, Portugal;2. Faculdade de Ciências, Universidade do Porto, Rua do Campo Alegre, s/n, PT4169-007 Porto, Portugal;3. Departamento de Ciencias de la Vida, Unidad Docente de Ecología, Facultad de Biología, Ciencias Ambientales y Química, Universidad de Alcalá, E-28805, Alcalá de Henares, Spain;4. Instituto de Recursos Naturales y Agrobiología de Sevilla (IRNAS-CSIC), E-41080 Sevilla, Spain;5. Department of Integrative Ecology, Estación Biológica de Doñana (EBD-CSIC), Avda. Américo Vespucio 26, Isla de la Cartuja, E-41092 Sevilla, Spain;6. Centre for Functional Ecology, Department of Life Sciences, University of Coimbra, Calçada Martim de Freitas, 3000-456, Coimbra, Portugal;7. Escola Superior Agrária, Instituto Politécnico de Coimbra, Bencanta, 3045-601, Coimbra, Portugal;8. Centre for Applied Ecology “Prof. Baeta Neves” (InBIO-CEABN), Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, PT1349-017 Lisboa, Portugal;9. Laboratory of Applied Ecology, CITAB ? Centre for the Research and Technology of Agro-Environment and Biological Sciences, University of Trás-os-Montes e Alto Douro, Vila Real, Portugal |
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| Abstract: | Control data are critical for improving areal interpolation results. Remotely sensed imagery, road network, and parcels are the three most commonly used ancillary data for areal interpolation of population. Meanwhile, the open access geographic data generated by social networks is emerging as an alternative control data that can be related to the distribution of population. This study evaluates the effectiveness of geo-located night-time tweets data as ancillary information and its combination with the three commonly used ancillary datasets in intelligent areal interpolation. Due to the skewed Twitter user age, the other purpose of this study is to test the effect of age bias control data on estimation of different age group populations. Results suggest that geo-located tweets as single control data does not perform as well as the three other control layers for total population and all age-specific population groups. However, the noticeable enhancement effect of Twitter data on other control data, especially for age groups with a high percentage of Twitter users, suggests that it helps to better reflect population distribution by increasing variation in densities within a residential area delineated by other control data. |
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| Keywords: | Areal interpolation Geo-located Twitter Remotely sensed imagery Volunteered geographic information |
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