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DC-04 - Social Media Platforms

Social media is a group of interactive Web 2.0 Internet-based applications that allow users to create and exchange user-generated content via virtual communities. Social media platforms have a large user population who generate massive amounts of digital footprints, which are valuable data sources for observing and analyzing human activities/behavior. This entry focuses on social media platforms that provide spatial information in different forms for Geographic Information Systems and Technology (GIS&T) research. These social media platforms can be grouped into six categories: microblogging sites, social networking sites, content sharing sites, product and service review sites, collaborative knowledge sharing sites, and others. Four methods are available for capturing data from social media platforms, including Web Application Programming Interfaces (Web APIs), Web scraping, digital participant recruitment, and direct data purchasing. This entry first overviews the history, opportunities, and challenges related to social media platforms. Each category of social media platforms is then introduced in detail, including platform features, well-known platform examples, and data capturing processes.

DA-33 - GIS&T in Urban and Regional Planning

Professionals within the urban and regional planning domain have long utilized GIS&T to better understand cities through mapping urban data, representing new proposals, and conducting modeling and analysis to help address urban problems. These activities include spatial data collection and management, cartography, and a variety of applied spatial analysis techniques. Urban and regional planning has developed the sub-fields of planning support systems and Geodesign, both of which describe a combination of technologies and methods to incorporate GIS&T into collaborative planning contexts. In the coming years, shifting patterns of global urbanization, smart cities, and urban big data present emerging opportunities and challenges for urban planning professionals.

PD-31 - PySAL and Spatial Statistics Libraries

As spatial statistics are essential to the geographical inquiry, accessible and flexible software offering relevant functionalities is highly desired. Python Spatial Analysis Library (PySAL) represents an endeavor towards this end. It is an open-source python library and ecosystem hosting a wide array of spatial statistical and visualization methods. Since its first public release in 2010, PySAL has been applied to address various research questions, used as teaching materials for pedagogical purposes in regular classes and conference workshops serving a wide audience, and integrated into general GIS software such as ArcGIS and QGIS. This entry first gives an overview of the history and new development with PySAL. This is followed by a discussion of PySAL’s new hierarchical structure, and two different modes of accessing PySAL’s functionalities to perform various spatial statistical tasks, including exploratory spatial data analysis, spatial regression, and geovisualization. Next, a discussion is provided on how to find and utilize useful materials for studying and using spatial statistical functions from PySAL and how to get involved with the PySAL community as a user and prospective developer. The entry ends with a brief discussion of future development with PySAL.

AM-107 - Spatial Data Uncertainty

Although spatial data users may not be aware of the inherent uncertainty in all the datasets they use, it is critical to evaluate data quality in order to understand the validity and limitations of any conclusions based on spatial data. Spatial data uncertainty is inevitable as all representations of the real world are imperfect. This topic presents the importance of understanding spatial data uncertainty and discusses major methods and models to communicate, represent, and quantify positional and attribute uncertainty in spatial data, including both analytical and simulation approaches. Geo-semantic uncertainty that involves vague geographic concepts and classes is also addressed from the perspectives of fuzzy-set approaches and cognitive experiments. Potential methods that can be implemented to assess the quality of large volumes of crowd-sourced geographic data are also discussed. Finally, this topic ends with future directions to further research on spatial data quality and uncertainty.

FC-42 - Distance Operations

Distance is a central concept in geography, and consequently, there are various types of operations that leverage the concept of distance. This short article introduces common distance measures, the purpose of distance operations, different types of operations and considerations, as well as sample applications in the physical and social domains. Distance operations can be performed on both vector or raster data, but the operations and results may differ. While performing distance operations, it is important to remember how distance is conceptualized while performing the operation.

FC-40 - Neighborhoods

Neighborhoods mean different things in varied contexts like computational geometry, administration and planning, as well as urban geography and other fields. Among the multiple contexts, computational geometry takes the most abstract and data-oriented approach: polygon neighborhoods refer to polygons sharing a boundary or a point, and point neighborhoods are defined by connected Thiessen polygons or other more complicated algorithms. Neighborhoods in some regions can be a practical and clearly delineated administration or planning units. In urban geography and some related social sciences, the terms neighborhood and community have been used interchangeably on many occasions, and neighborhoods can be a fuzzy and general concept with no clear boundaries such that they cannot be easily or consensually defined. Neighborhood effects have a series of unique meanings and several delineation methods are commonly used to define social and environmental effects in health applications.

CP-05 - Geospatial Technology Transfer Opportunities, and a Case Study of the Taghreed System

The technology transfer process moves research ideas from preliminary stages in research labs and universities to industrial products and startup companies. Such transfers significantly contribute to producing new computing platforms, services, and geospatial data products based on state-of-the-art research. To put technology transfer in perspective, this entry highlights key lessons learned through the process of transferring the Taghreed System from a research and development (R&D) lab to an industrial product. Taghreed is a system that supports scalable geospatial data analysis on social media microblogs data. Taghreed is primarily motivated by the large percentage of mobile microblogs users, over 80%, which has led to greater availability of geospatial content in microblogs beyond anytime in the digital data history. Taghreed has been commercialized and is powering a startup company that provides social media analytics based on full Twitter data archive.