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Abstract Nighttime lights (NTL) imagery is one of the most commonly used tools to quantitatively study socioeconomic systems over large areas. In this study we aim to use location-based social media big data to challenge the primacy of NTL imagery on estimating socioeconomic factors. Geo-tagged tweets posted in the contiguous United States in 2013 were retrieved to produce a tweet image with the same spatial resolution of the NTL imagery (i.e., 0.00833° × 0.00833°). Sum tweet (the total number of tweets) and sum light (summed DN value of the NTL image) of each state or county were obtained from the tweets and the NTL images, respectively, to estimate three important socioeconomic factors: personal income, electric power consumption, and fossil fuel carbon dioxide emissions. Results show that sum tweet is a better measure of personal income and electric power consumption while carbon dioxide emissions can be more accurately estimated by sum light. We further exploited that African-Americans adults are more likely than White seniors to post geotagged tweets in the US, yet did not find any significant correlations between proportions of the subpopulations and the estimation accuracy of the socioeconomic factors. Existence of saturated pixels and blooming effects and failure to remove gas flaring reduce quality of NTL imagery in estimating socioeconomic factors, however, such problems are nonexistent in the tweet images. This study reveals that the number of geo-tagged tweets has great potential to be deemed as a substitute of brightness of NTL to assess socioeconomic factors over large geographic areas. [ABSTRACT FROM AUTHOR]

The wide penetration of location-aware mobile devices and location-based services renders the location-based social media as a reliable proxy to study the real-world geographic space. Language diversity is an important indicator of a city's internationalization level. People communicate using different languages in the cyberspace of social media as they do in the geographic space. The location-based social media therefore provides an innovative set of lens to map the language diversity and study the internationalization of cities. In the enclosed graphics, based on a collection of geo-tagged Twitter posts, we generated a fine resolution map of language diversity index in the area of Hong Kong to illustrate the potential of location-based social media in city research. [ABSTRACT FROM AUTHOR]

Characterizing human mobility patterns is essential for understanding human behaviors and the interactions with socioeconomic and natural environment, and plays a critical role in public health, urban planning, transportation engineering and related fields. With the widespread of location-aware mobile devices and continuing advancement of Web 2.0 technologies, location-based social media (LBSM) have been gaining widespread popularity in the past few years. With an access to locations of hundreds of million users, profiles and the contents of the social media posts, the LBSM data provided a novel modality of data source for human mobility study. By exploiting the explicit location footprints and mining the latent demographic information implied in the LBSM data, the purpose of this paper is to investigate the spatiotemporal characteristics of human mobility with a particular focus on the impact of demography. To serve this purpose, we first collect geo-tagged Twitter feeds posted in the conterminous United States area, and organize the collection of feeds using the concept of space-time trajectory corresponding to each Twitter user. Commonly human mobility measures, including detected home and activity centers, are derived for each user trajectory. We then select a subset of Twitter users that have detected home locations in the city of Chicago as a case study, and apply name analysis to the names provided in user profiles to learn the implicit demographic information of Twitter users, including race/ethnicity, gender and age. Finally we explore the spatiotemporal distribution and mobility characteristics of Chicago Twitter users, and investigate the demographic impact by comparing the differences across three demographic dimensions (race/ethnicity, gender and age). We found that, although the human mobility measures of different demographic groups generally follow the generic laws (e.g., power law distribution), the demographic information, particular the race/ethnicity group, significantly affects the urban human mobility patterns. [ABSTRACT FROM AUTHOR]

Location-based social media have been extensively utilized in the concept of “social sensing” to exploit dynamic information about human activities, yet joint uses of social sensing and remote sensing images are underdeveloped at present. In this paper, the close relationship between the number of Twitter users and brightness of nighttime lights (NTL) over the contiguous United States is calculated and geotagged tweets are then used to upsample a stable light image for 2013. An associated outcome of the upsampling process is the solution of two major problems existing in the NTL image, pixel saturation, and blooming effects. Compared with the original stable light image, digital number (DN) values of the upsampled stable light image have larger correlation coefficients with gridded population (0.47 versus 0.09) and DN values of the new generation NTL image product (0.56 versus 0.52), i.e., the Visible Infrared Imaging Radiometer Suite day/night band image composite. In addition, total personal incomes of states are disaggregated to each pixel in proportion to the DN value of the pixel in the NTL images and then aggregate by counties. Personal incomes distributed by the upsampled NTL image are closer to the official demographic data than those distributed by the original stable light image. All of these results explore the potential of geotagged tweets to improve the quality of NTL images for more accurately estimating or mapping socioeconomic factors. [ABSTRACT FROM AUTHOR]