Technical geography is the branch of geography that involves using, studying, and creating tools to obtain, analyze, interpret, understand, and communicate spatial information. The spatial data types a technical geographer employs may vary widely, including human and physical geography topics, with the common thread being the techniques and philosophies employed. To accomplish this, technical geographers often create their own software or scripts, which can then be applied more broadly by others. They may also explore applying techniques developed for one application to another unrelated topic, such as applying Kriging, originally developed for mining, to disciplines as diverse as real-estate prices. Further, a technical geographer may explore the relationship between the spatial technology and the end users to improve upon the technology and better understand the impact of the technology on human behavior.

The other branches of geography, most commonly limited to human geography and physical geography, make extensive use of technical geographies concepts and techniques. Nevertheless, the methods and theory are distinct, and a technical geographer may be more concerned with the technological and theoretical concepts than the nature of the data. In teaching technical geography, instructors often rely on examples from human and physical geography to explain theoretical concepts. While technical geography mostly works with quantitative data, the techniques and technology can be applied to qualitative geography, differentiating it from quantitative geography. Within the branch of technical geography are the major and overlapping subbranches of geographic information science, geomatics, and geoinformatics.

Technical geography is highly theoretical and focuses on developing and testing methods and technologies for handling spatial-temporal data. These technologies are then applied to datasets and problems within the branches of both human and physical geography. Historically, technical geography was focused on cartography and globe-making. Today, while technical geographers still develop and make maps, the Information Age has pushed the development of information management techniques to handle spatial data and support decision-makers. To this end, technical geographers often adapt technology and techniques from other disciplines to spatial problems rather than create original innovations, such as using computers to aid in cartography. They also explore adapting techniques developed for one area of geography to another, such as kriging, created for estimating gold ore distributions but now applied to topics such as real estate appraisal. Technical geography today is theoretically grounded in information theory, or the study of mathematical laws that govern information systems.

Several concepts in technical geography are considered central attributes of the discipline. In one paper, autocorrelation and frequency are listed as concepts on which technical geography is based. Central to technical geography are the technologies surrounding cartography and map production, which are only possible through cartographic generalization. More than just reducing the overall level of information, cartographic generalization helps discover patterns and trends in data that underlie many techniques and technologies employed and investigated by technical geographers.

Autocorrelation is a statistical measure used to assess the degree to which a given data set is correlated with itself over different time intervals or spatial distances. In essence, it quantifies the similarity between observations as a function of the time lag or spatial distance between them. Autocorrelation can be positive (indicating that similar values cluster together) or negative (indicating that dissimilar values are near each other). Spatial autocorrelation involves the correlation of a variable with itself across different spatial locations. Temporal autocorrelation involves the correlation of a signal with a delayed copy of itself over successive time intervals. Autocorrelation is the foundation of Tobler's first law of geography. Spatial autocorrelation is measured with tools such as Moran's I or Getis–Ord statistics.

Autocorrelation is fundamental to technical geography because it provides critical insights into the spatial and temporal structure of geographical data. It enhances the ability to model, analyze, and interpret spatial patterns and relationships, supporting various applications from environmental monitoring and urban planning to resource management and public health. By understanding and leveraging autocorrelation, geographers can make more informed decisions, improve the accuracy of their analyses, and contribute to solving real-world geographical problems. The techniques and technologies used to leverage this understanding are a core focus of technical geography.

In statistics, frequency refers to the number of occurrences of a particular event or value within a dataset. When dealing with spatial and temporal datasets, the concept of frequency can be applied to understand how often certain events or values occur across different locations (spatial) or over time (temporal). Spatial datasets contain data points that are associated with specific geographic locations, and frequency in spatial datasets can be used to analyze patterns and distributions across different areas. Temporal datasets involve data points that are associated with specific time points, and frequency in temporal datasets helps analyze trends and patterns over time. Analyzing how the frequency of events changes across both space and time can reveal dynamic patterns. Spatial and temporal frequency are core concepts in technical geography because they are fundamental to understanding and analyzing geographic phenomena. Geography is inherently concerned with the distribution and dynamics of features across space and over time, and technical geography researches and develops the techniques to deal with this data.

Cartographic generalization is the process of simplifying the representation of geographical information on maps, making complex data more understandable and useful for specific purposes or scales. This process involves selectively reducing feature detail to prevent clutter and ensure the map communicates the intended information effectively. The need for generalization arises because maps often depict large areas and scales, where including every detail is impractical and can overwhelm the map reader. The primary goal of cartographic generalization is to balance detail with readability, ensuring that the map serves its intended purpose without sacrificing essential information. By placing data in a spatial context, even though it is generalized, cartographic generalization creates additional information by revealing patterns and trends in the data.