led to the rethinking of the role of human cultural knowledge. The human terrain program brought together information technology and a vast array of regional sociocultural information that had been scraped from the web, provided through social media, gathered from other open sources, and collected in the field. The data were analyzed using search and comparison techniques, social network analytics, geographic visualization, and statistics. The aim was to provide up-to-date, accurate information about the general sociocultural environment, current opinion leaders and persons with power, and climate, economic, and political conditions.
Increasingly, sociocultural information, both historical and current, is being placed on maps. New technologies that admit location capture (e.g., modern cell phones) are increasing the amount of location-based data on social and social interaction. Crowdsourcing, Ushahidi-style data captures (e.g., reports submitted by local observers via mobile phone or the Internet), location-based twitters, and so on are providing unprecedented levels of sociocultural information that is at least partially spatially tagged. With new data come new research opportunities and the ability to understand how space constrains and enables social and cultural activity. Illustrative new areas of research include geotemporal social media sampling, location identification from texts, and geonetwork analytics. The next decade will likely see major changes in the quality of sociospatial data presentation and new technologies for capturing, assessing, visualizing, and forecasting social data with a spatiotemporal context.
Knowledge and Skills
Human geography involves four main components:
1. Geo-enabled network analysis—mapping the network of who, what, how, why, and when to locations (e.g., the al-Qaeda social network).
2. Sentiment and technology dispersion—mapping the movement of ideas, activities, technologies, and beliefs as they move from location to location (e.g., the spread of revolution in the Middle East during the Arab Spring).
3. Cultural geography overviews—compendiums of diverse information on current leaders, languages, foods, habits, religions, etc., which are increasingly taking the form of web-based mashups. Such overviews and the tools for analyzing them formed the basis of human terrain efforts during the Iraq and Afghanistan wars.
4. Sociolinguistic ethnic characterizations— mapping which families, clans, and tribes are where (e.g., the tribal sociolinguistic heredity network).
Each of these areas requires different expertise. Some areas require technical expertise (e.g., programming, scripting) while others require the mastery of advanced conceptual frameworks and approaches (e.g., agentbased modeling, network analysis). These skills are not generally acquired in traditional courses on sensor assessment, cartography, or map interpretation.
An important skill in human geography is text mining: the process of deriving high-quality information from textual sources for analysis. Text, such as news articles, books, twitter feeds, and blogs, contain information about differences in the human condition across locations. Techniques for mining text are reasonably accurate for extracting the names of people, organizations, and locations from English texts. However, challenges remain in interpreting multiple languages, identifying the location of places, and distinguishing between place and person names (e.g., the city of Dorothy Pond, Massachusetts) and place and organization names (e.g., the White House). Both geographical expertise and text-mining expertise are needed to address these problems.
Education and Professional Preparation Programs
A comprehensive human geography program covers five core elements: (1) collection and coding of geomarked human data, (2) geo-enabled text analysis, (3) geo-enabled network analysis and dynamic network analysis, (4) computer simulation of human geography data and forecasts, and (5) geocultural analysis and overviews. Each of these has an associated set of methods and tools that students need to learn, including (1) tools for collecting social media and news data (e.g., TweetTracker, REA); (2) tools for natural language processing, text mining, and sentiment mining (e.g., AutoMap); (3) tools for metanetwork analytics and visualization (e.g., ORA, R); (4) tools for developing