LEARNING OBJECTIVES 1. Define and describe spatial analysis. 2. Describe the trends and significa... more LEARNING OBJECTIVES 1. Define and describe spatial analysis. 2. Describe the trends and significant developments in spatial analysis. 3. Define, describe, and illustrate key spatial concepts. 4. Learn about the unique properties of spatial data and inherent challenges. In conventional terms, geographers regard spatial analysis as a broad and comprehensive undertaking that entails the use of well-established ana-lytical/visualization tools and procedures to analyze and synthesize loca-tionally referenced data. The approaches are rigorous and are drawn from statistical, mathematical, and geographical principles to conduct a systematic examination of spatial patterns and processes, including the exploration of interactions between space and time. Studying the locational and distribu-tional arrangement of objects, people, events, and processes in space, and the underlying factors that account for these arrangements are some of the analytical goals of a geospatial data scientist. The work requires a place-based mindset with emphasis on uncovering spatial patterns and spatial linkages, and examining spatial behaviors and complex interactions within and across locations that result in these distributional patterns. Engaging in spatial analysis typically requires the use of quantitative data in a digital format, but increasingly data scientists are devising interesting and creative ways to integrate qualitative and contextual data into the analysis. Once a research project is defined with the articulation of a clear set of goals, objectives, and research questions, the data scientist begins by systematically choosing the appropriate units of observation from which to collect the data, the spatial scales at which they will be measured, and the variables and means by which the data values will be assigned to those variables. The field of spatial analysis is inspired by a strong logical positivist tradition that involves inductive and deductive reasoning, hypothesis testing, and
LEARNING OBJECTIVES 1. Define and describe spatial analysis. 2. Describe the trends and significa... more LEARNING OBJECTIVES 1. Define and describe spatial analysis. 2. Describe the trends and significant developments in spatial analysis. 3. Define, describe, and illustrate key spatial concepts. 4. Learn about the unique properties of spatial data and inherent challenges. In conventional terms, geographers regard spatial analysis as a broad and comprehensive undertaking that entails the use of well-established ana-lytical/visualization tools and procedures to analyze and synthesize loca-tionally referenced data. The approaches are rigorous and are drawn from statistical, mathematical, and geographical principles to conduct a systematic examination of spatial patterns and processes, including the exploration of interactions between space and time. Studying the locational and distribu-tional arrangement of objects, people, events, and processes in space, and the underlying factors that account for these arrangements are some of the analytical goals of a geospatial data scientist. The work requires a place-based mindset with emphasis on uncovering spatial patterns and spatial linkages, and examining spatial behaviors and complex interactions within and across locations that result in these distributional patterns. Engaging in spatial analysis typically requires the use of quantitative data in a digital format, but increasingly data scientists are devising interesting and creative ways to integrate qualitative and contextual data into the analysis. Once a research project is defined with the articulation of a clear set of goals, objectives, and research questions, the data scientist begins by systematically choosing the appropriate units of observation from which to collect the data, the spatial scales at which they will be measured, and the variables and means by which the data values will be assigned to those variables. The field of spatial analysis is inspired by a strong logical positivist tradition that involves inductive and deductive reasoning, hypothesis testing, and
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