This entry reviews the use of GIS&T in the digital humanities and in the spatial humanities, highlighting opportunities for interdisciplinary collaborations between GIScientists and humanities scholars, including in history, archeology, and literary studies. Challenges are highlighted as well, including epistemological and ontological differences between the spatial, abstract, and quantitative view of the world of GIS&T and GIScience and the humanities emphasis on place and qualitative methods. The potential of mixed methods to bring together different epistemological perspectives is discussed in this context. Scale is identified as a promising geographical framework for humanities research, both in its metaphorical aspects and as intended in cartography. Examples of the use of GIS&T and GIScience in the humanities are provided, including historical GIS, geohistorical gazetteers, archeology and GIS, and GIS in literary studies. The entry is framed historically, with reference to the work of Bakhtin, Braudel, and Hägerstrand, who are early influencers of the spatial turn in the humanities. Among the research directions briefly explored are the GIS of place, deep maps, and qualitative GIS, which exemplify how the collaboration between GIScience and the humanities can be strengthened.

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.

GIS&T project planning and management falls under the broader category of project management (PM) in general and information technology (IT) PM in particular, providing a rich background and guidelines that are stewarded by associations and their certifications. The lifecycle of a project or its component phases involves a number of process groups involving a series of actions leading to a result that are sequenced in the following manner: initiating, planning, executing and controlling, and closing. Effective project planning and management requires understanding of its knowledge areas in the project management body of knowledge (PM BoK), which include integration, scope, time, cost, quality, human resource, communications, risk, procurement, and stakeholder management. Numerous tools and techniques are available to assist the project manager in planning, executing, and controlling these efforts, some of which are specific to GIS&T projects. The distinctiveness of GIS&T project planning and management lies in an understanding of the uniqueness, overlap and connections that exist between the PM BoK and the GIS&T BoK, both of which have achieved new levels of maturity in recent decades. 

Spatial association broadly describes how the locations and values of samples or observations vary across space. Similarity in both the attribute values and locations of observations can be assessed using measures of spatial association based upon the first law of geography. In this entry, we focus on the measures of spatial autocorrelation that assess the degree of similarity between attribute values of nearby observations across the entire study region. These global measures assess spatial relationships with the combination of spatial proximity as captured in the spatial weights matrix and the attribute similarity as captured by variable covariance (i.e. Moran’s I) or squared difference (i.e. Geary’s C). For categorical data, the join count statistic provides a global measure of spatial association. Two visualization approaches for spatial autocorrelation measures include Moran scatterplots and variograms (also known as semi-variograms).