a b s t r a c tIn the past several years, social media (e.g., Twitter and Facebook) has experienced a spectacular rise in popularity and has become a ubiquitous location for discourse, content sharing and social networking. With the widespread adoption of mobile devices and location-based services, social media typically allows users to share the whereabouts of daily activities (e.g., check-ins and taking photos), thus strengthening the role of social media as a proxy for understanding human behaviors and complex social dynamics in geographic spaces. Unlike conventional spatiotemporal data, this new modality of data is dynamic, massive, and typically represented in a stream of unstructured media (e.g., texts and photos), which pose fundamental representation, modeling and computational challenges to conventional spatiotemporal analysis and geographic information science. In this paper, we describe a scalable computational framework to harness massive location-based social media data for efficient and systematic spatiotemporal data analysis. Within this framework, the concept of space-time trajectories (or paths) is applied to represent activity profiles of social media users. A hierarchical spatiotemporal data model, namely a spatiotemporal data cube model, is developed based on collections of space-time trajectories to represent the collective dynamics of social media users across aggregation boundaries at multiple spatiotemporal scales. The framework is implemented based upon a public data stream of Twitter feeds posted on the continent of North America. To demonstrate the advantages and performance of this framework, an interactive flow mapping interface (including both single-source and multiple-source flow mapping) is developed to allow real-time and interactive visual exploration of movement dynamics in massive location-based social media data at multiple scales.
Background: Geospatial information has drawn substantial attention as a means for building a common measurement and monitoring framework that can be employed across different countries all over the world for the sustainable development goals (SDGs) of the United Nations (UN). Determining the appropriate spatial units for measurements is a critical issue, particularly for the goals associated with the safety, resilience, and sustainability of cities and human settlements. Open geospatial technologies are expected to help address this issue of spatial measurement since they comply with geospatial standards and can be easily adopted by both developed and developing countries. Methods: This study evaluates the applicability of open geospatial technologies for the development of a common measurement framework for UN SDG 11. To this end, the study analyzes to what extent national urban information systems of Korea can support the measurement of the target goals and makes recommendations for how open geospatial data and technologies could fill the void that current systems cannot fill at present.
SUMMARY Social media have experienced a spectacular rise in popularity, attracting hundreds of millions of users and generating unprecedented amount of content that increasingly contain location and place information. Collectively, the massive location information in these data provides an excellent opportunity to better understand many geographic phenomena and geospatial dynamics in a timely fashion. Recent studies capitalizing on social networking and media data show significant societal impacts in many areas including prediction of stock market and infectious disease surveillance. However, because location‐based social media data are often massive, generated dynamically, and unstructured, significant computation, data, and visualization challenges need to be resolved. This research aims to demonstrate the use of massive social media data to interactively analyze spatiotemporal events across spatial and temporal scales, by establishing a data‐driven framework using cyberGIS—geographic information systems (GIS) based on advanced cyberinfrastructure—to resolve aforementioned challenges. Specifically, FluMapper—an application on the CyberGIS Gateway—is employed as a case study to demonstrate the data‐driven framework and seamless integration of massive location‐based social media data and spatial analytical services within the online problem solving environment of the Gateway. FluMapper presents integrated results from two complementary spatial analyses: (i) an interactive exploration of spatial distribution of flu risk and (ii) dynamic mapping of movement patterns, across multiple spatial, and temporal scales. The seamless integration of these two analyses through the framework illustrates the potential of cyberGIS to resolve the compute and data challenges of analyzing near real‐time social media data in an efficient and scalable manner and to support interactive visualization. Copyright © 2014 John Wiley & Sons, Ltd.
Spatial profiling of community food security data can help the targeting of geographic areas and populations most vulnerable to food insecurity. While multiple poverty mapping systems support spatial profiling, they often lack capabilities to disseminate mapping results to a wide range of audiences and to spatially link qualitative data to quantitative analysis. To address these limitations, this study presents a web mapping framework which integrates a variety of publicly available software tools to enable spatial exploration of both quantitative and qualitative data. Specifically, our framework allows online choropleth mapping and thematic data exploration through a mixture of free mapping Application Programming Interfaces (APIs) and open source software tools for spatial data processing and desktop-like user interfaces. The study demonstrates this framework by developing a web prototype for informing food insecurity issues in Bogotá, Colombia. The prototype implementation reveals that the proposed framework facilitates the development of scalable and functionally-extensible mapping systems and the identification of community-specific food insecurity problems (e.g., food kitchens inaccessible from workplaces of lowincome residents). This suggests that web-based cartographic visualization using publicly available software tools can be useful for spatial examination of community food insecurity as well as for cost-effective distribution of the resulting map information.
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