Geographic Information Science and Technology (GIS&T) has a long-running tradition of using spatially-oriented methodologies and representational techniques such as cartography and mapping to address hazards and disasters. This tradition remains important as ever as global society faces newer and more complex challenges resulting from climate change and new challenges such as the COVID-19 pandemic. GIS&T has become an invisible technology within the disaster management cycle of planning and preparedness, response, recovery, and mitigation. Spatial technologies such as geographic information systems (GIS), remote sensing techniques, spatial data science, artificial intelligence, and machine learning are now widespread and pervasive. Despite these advancements, there is more that can be done to incorporate GIS&T perspectives into disaster management. In this article, we outline important conceptual ideas to consider on the use of GIS&T for disaster management, disaster management organizations that use GIS&T, and practical information to orient newcomers to this exciting and important interdisciplinary combination.

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.

Linking crime and place has been the objective of crime mapping since the early nineteenth century. Contemporary scholars have since investigated spatio-temporal crime patterns to explain why crime concentrates in certain places during certain times. Collectively, this body of research has identified various environmental and situational factors that contribute to the formation of crime hot spots and spawned widespread crime prevention and reduction strategies commonly referred to as place-based policing.  Environmental criminology guides the bulk of this crime-and-place research and provides a means for interpreting place and crime. The chapter details theories behind place-based policing, examples of place-based policing strategies that leverage geographic information science and its associated technologies (GIS&T), and relevant data visualization tools used by law enforcement to implement place-based strategies to address crime.

Geographic Information System (GIS) applications often involve various analytical techniques and geographic data to produce thematic maps for gaining a better understanding of geospatial situations to support spatial decisions. Accuracy assessment of a thematic map is necessary for evaluating the quality of the research results and ensuring appropriate use of the geographic data. Thematic accuracy deals with evaluating the accuracy of the attributes or labels of mapped features by comparing them to a reference that is assumed to be true. The fundamental practice presents the remote sensing approach to thematic accuracy assessment as a good guidance. For instance, the accuracy of a remote sensing image can be represented as an error matrix when the map and reference classification are conducted based on categories. This entry introduces basic concepts and techniques used in conducting thematic accuracy with an emphasis on land cover classification based on remote sensing images. The entry first introduces concepts of spatial uncertainty and spatial data quality standards and further gives an example of how spatial data quality affects thematic accuracy. Additionally, the entry illustrates the techniques that can be used to access thematic accuracy as well as using spatial autocorrelation in thematic accuracy sampling design.