The study of location and location-based phenomena is a flourishing field. Many universities have grown their research and/or services in this field (often called GIS), established centers that are primarily engaged in the research of GIS, or applying GIS technologies to support researches of other fields. Some straddle “research of” and “research with” GIS in the same center, engaging in both GIScience research, often by researchers in a department or school, and geospatial technology services, often for users across the university. We conducted an online survey to scour the landscape of such centers in universities worldwide, to understand how they are structured, managed, financed, and sustained. The survey also included units as part of a library, department, or lab. Eighty-one valid responses were analyzed, revealing these organizations’ administrative, financial, staffing, and operational status; their history, visions, responsibilities, resources, constrains, challenges, and opportunities. The result showed differences between universities with and without a geography department.
Despite several decades of discussion and debate around the role of GIS in the discipline of Geography, it would be a stretch to argue that GIS has not irreversibly altered the discipline, both in the scope of research and teaching as well as in the wider imagination of a general public. However, it remains a challenge to incorporate the range of geographic knowledge, born of a diversity of modalities, into operational insights and analytical pre-conditions in a GIS. To be certain, some irreconcilability between GIS and geographical inquiry is to be expected, epistemologically speaking. In what follows, we consider what might be meant by a shift to geographic analysis as scholars from disciplines in the humanities and social sciences turn to GIS as a method of observation, interpretation, analysis, and representation. In this context, we engage in a thought experiment and offer some commentary, fixing the notion of information system, while opening the geographic in GIS to more variable understanding. The point is to pursue greater development of GIS theory and method, encompassing, while not reducing, scientific, social scientific, and humanities research.
Recently declassified photographs taken by U2 spy planes in the 1950s and 1960s provide an important new source of historical aerial imagery useful for Eurasian archaeology. Like other sources of historical imagery, U2 photos provide a window into the past, before modern agriculture and development destroyed many archaeological sites. U2 imagery is older and in many cases higher resolution than CORONA spy satellite imagery, the other major source of historical imagery for Eurasia, and thus can expand the range of archaeological sites and features that can be studied from an aerial perspective. However, there are significant barriers to finding and retrieving U2 imagery of particular locales, and archaeologists have thus not yet widely used it. In this article, we aim to reduce these barriers by describing the U2 photo dataset and how to access it. We also provide the first spatial index of U2 photos for the Middle East. A brief discussion of archaeological case studies drawn from U2 imagery illustrates its merits and limitations. These case studies include investigations of prehistoric mass-kill hunting traps in eastern Jordan, irrigation systems of the first millennium BC Neo-Assyrian Empire in northern Iraq, and twentieth-century marsh communities in southern Iraq.
The discipline of the humanities has long been inseparable from the exploration of space and time. With the rapid advancement of digitization, databases, and data science, humanities research is making greater use of quantitative spatiotemporal analysis and visualization. In response to this trend, our team developed the Chinese academic map publishing platform (AMAP) with the aim of supporting the digital humanities from a Chinese perspective. In compiling materials mined from China’s historical records, AMAP attempts to reconstruct the geographical distribution of entities including people, activities, and events, using places to connect these historical objects through time. This project marks the beginning of the development of a comprehensive database and visualization system to support humanities scholarship in China, and aims to facilitate the accumulation of spatiotemporal datasets, support multi-faceted queries, and provide integrated visualization tools. The software itself is built on Harvard’s WorldMap codebase, with enhancements which include improved support for Asian projections, support for Chinese encodings, the ability to handle long text attributes, feature level search, and mobile application support. The goal of AMAP is to make Chinese historical data more accessible, while cultivating collaborative opensource software development.
The goal of this chapter is to evaluate and compare Geospatial Software as a Service (GSaaS) platforms oriented toward providing basic mapping capabilities to non-GIS experts. These platforms allow users to organize spatial materials in layers, perform overlay and basic visual analysis, and share both final maps and the processes used to create them with remote collaborators. The authors gathered data on the characteristics of 15 platforms through an online survey, then summarized the results and created an Excel tool to enable users to sift through the data to identify platforms based on need. This study presents a snapshot of the current GSaaS landscape, summarizes current capabilities, points out weaknesses, and considers the potential of this class of application.
Disasters have substantial consequences for population mental health. Social media data present an opportunity for mental health surveillance after disasters to help identify areas of mental health needs. We aimed to 1) identify specific basic emotions from Twitter for the greater New York City area during Hurricane Sandy, which made landfall on October 29, 2012, and to 2) detect and map spatial temporal clusters representing excess risk of these emotions.
We applied an advanced sentiment analysis on 344,957 Twitter tweets in the study area over eleven days, from October 22 to November 1, 2012, to extract basic emotions, a space-time scan statistic (SaTScan) and a geographic information system (QGIS) to detect and map excess risk of these emotions.
Sadness and disgust were among the most prominent emotions identified. Furthermore, we noted 24 spatial clusters of excess risk of basic emotions over time: Four for anger, one for confusion, three for disgust, five for fear, five for sadness, and six for surprise. Of these, anger, confusion, disgust and fear clusters appeared pre disaster, a cluster of surprise was found peri disaster, and a cluster of sadness emerged post disaster.
We proposed a novel syndromic surveillance approach for mental health based on social media data that may support conventional approaches by providing useful additional information in the context of disaster. We showed that excess risk of multiple basic emotions could be mapped in space and time as a step towards anticipating acute stress in the population and identifying community mental health need rapidly and efficiently in the aftermath of disaster. More studies are needed to better control for bias, identify associations with reliable and valid instruments measuring mental health, and to explore computational methods for continued model-fitting, causal relationships, and ongoing evaluation. Our study may be a starting point also for more fully elaborated models that can either prospectively detect mental health risk using real-time social media data or detect excess risk of emotional reactions in areas that lack efficient infrastructure during and after disasters. As such, social media data may be used for mental health surveillance after large scale disasters to help identify areas of mental health needs and to guide us in our knowledge where we may most effectively intervene to reduce the mental health consequences of disasters.
The authors obtained the raw dataset from Harvard Center for Geographical Analysis (CGA, http://www.gis.harvard.edu), the institution that collected the data. The authors did not have a role in the data collection. Since the raw dataset contains detailed information of individuals at a high spatio-temporal resolution, and since the data is owned solely by Twitter users (see Twitter's data sharing policy), the authors consider the raw dataset to be third-party data, which unfortunately cannot be made publicly available. The authors had access to the raw tweet data from CGA because they are researchers at Harvard and formally agreed not to share the raw data publicly. However, the authors appreciate transparency in research and are ready to make the Tweet IDs used in the current study available, according to Twitter's sharing policy. With information of the Tweet IDs, researchers will be able to look up those IDs using the Twitter API to get full tweets for verifying the conclusions made in the paper. In addition, the authors can make the raw data available to researchers who have a formal affiliation with Harvard University. Harvard also provides a rehydration app for independent researchers to facilitate conversions of TweetIDs back to full tweets, https://github.com/cga-harvard/hhypermap-bop/tree/master/BOP-utilities/T.... Please direct inquiries for IDs or the raw dataset to Wendy Guan (email@example.com) or Benjamin Lewis (firstname.lastname@example.org) of CGA, or to the corresponding author (email@example.com).