Forecasting political elections has attracted a lot of attention. Traditional election forecasting models in political science generally take preference in poll surveys and economic growth at the national level as the predictive factors. However, spatially or temporally dense polling has always been expensive. In the recent decades, the exponential growth of social media has drawn enormous research interests from various disciplines. Existing studies suggest that social media data have the potential to reflect the political landscape. Particularly, Twitter data have been extensively used for sentiment analysis to predict election outcomes around the world. However, previous studies have typically been data-driven and the reasoning process was oversimplified without robust theoretical foundations. Most of the studies correlate twitter sentiment directly and solely with the election results which can hardly be regarded as predictions. To develop a more theoretically plausible approach this study draws on political science prediction models and modifies them in two aspects. First, our approach uses Twitter sentiment to replace polling data. Second, we transform traditional political science models from the national level to the county level, the finest spatial level of voting counts. The proposed model has independent variables of support rate based on Twitter sentiment and variables related to economic growth. The dependent variable is the actual voting result. The 2016 U.S. presidential election data in Georgia is used to train the model. Results show that the proposed modely is effective with the accuracy of 81% and the support rate based on Twitter sentiment ranks the second most important feature.

Social media platforms have become a critical virtual community where people share information and discuss issues. Their capabilities for fast dissemination and massive participation have placed under scrutiny the way in which they influence people’s perceptions over time and space. This paper investigates how El Niño, an extreme recurring weather phenomenon, was discussed on Twitter in the United States from December 2015 to January 2016. A multiple-dimensional analysis, including spatial, social, temporal, and semantic perspectives, is conducted to comprehensively understand Twitter users’ discussion of such weather phenomenon. We argue that such multi-dimensional analysis can reveal complicated patterns of Twitter users’ online discussion and answers questions that cannot be addressed with a single-dimension analysis. For example, a significant increase in tweets about El Niño was noted when a series of rainstorms inundated California in January 2016. Some discussions on natural disasters were influenced by their geographical distances to the disasters and the prevailing geopolitical environment. The popular tweets generally discussing El Niño were overall negative, while tweets talking about how to prepare for the California rainstorms were more positive.

People interact with each other in space and time. Improved understanding of human interactions in spatial, temporal, and social dimensions are highly beneficial for research and practices in public health, urban planning, and other fields. Traditional methods of collecting social interaction data are time-intensive and resource-consuming, resulting in relatively small sample sizes and limited information. Furthermore, traditional methods often oversimplify the dynamics of human interactions and fail to capture the characteristics of places where the interactions occur. With the popularity of location-based social media (LBSM) platforms, people can publish information about their social events such as time, location, and other participants. This research introduces a framework that formalizes terminologies and concepts related to spatial-social connections for the construction of spatial-social networks from LBSM data in GIS. Supported by the framework, the study presents methods of collecting, analyzing, and visualizing LBSM data in spatial-social dimensions. The methods are implemented and tested in a case study with Facebook data. The case study demonstrates that location-based social media data can be transformed into spatial-social networks and then be analyzed and visualized to answer innovative types of scientific inquiries.

On March 11, 2020, the World Health Organization announced the novel coronavirus disease (COVID-19) as a pandemic. Despite an increasing number of international attempts using maps to present and communicate COVID-19-related information in different organizations, most map products have only used the presentation function of maps. Against this backdrop, we offer an automatically daily-updated, color-blind-friendly, Tableau-based interactive dashboard to demonstrate where and how different countries are fighting against COVID-19. The dashboard allows users to specify countries they want to compare and aggregate relevant data on a daily, weekly, or monthly basis.

Transit planning practice majorly concerns transit route optimization, while the spatial distribution of bus terminals is generally neglected. Large cities have large-scale transit system, and their transit terminal distributions are complex. This paper explores the spatial characteristics of bus terminal distribution, and proposes a two-stage method for designing evenly distributed bus terminals and terminal zones. The study is based on Wuhan, a metropolitan city in central China. Firstly, the characteristics of spatial distribution of bus terminals in Wuhan are analyzed, including the spatial density distribution, as well as the relationships with urban morphology, land use, road network and service intensity. Secondly, a two-stage approach is proposed for locating bus terminals under ideal situation. Terminals from the two stages may constitute a useful supplement for bus route deployment.