Eugenio Arima

Computer programming and scripting applied to geospatial data.

Geography 350E and 356T (Topic: Geoprocessing) may not both be counted.

Prerequisite: Upper-division standing and Geography 460G.

Offered on the letter-grade basis only.

Computer programming and scripting applied to geospatial data.

Geography 350E and 356T (Topic: Geoprocessing) may not both be counted.

Prerequisite: Upper-division standing and Geography 460G.

Offered on the letter-grade basis only.

Computer programming and scripting applied to geospatial data.

Geography 350E and 356T (Topic: Geoprocessing) may not both be counted.

Prerequisite: Upper-division standing and Geography 460G.

Offered on the letter-grade basis only.

Prerequisite: Graduate standing; additional prerequisites vary with the topic.

Designed to accommodate 35 or fewer students. Course number may be repeated for credit when the topics vary.

This course will overview a wide range of statistical regression techniques applied to the social sciences and spatial contexts, including ordinary least squares, limited dependent variable models, and spatial regression models. The goal of the course is to equip geography masters and PhD students with sufficient knowledge of regression analysis to a) employ those methods in their own research, b) help them understand the empirical literature they read, and c) provide a basis for more advanced methods. Emphasis will be placed on the meaning of the assumptions needed to obtain consistent and/or unbiased estimators and cases when those assumptions may be violated. Although knowledge of college algebra and basic probability and statistics is required, the course will approach regression analysis empirically, with substantial time devoted to lab exercises, implementation of models, and interpretation of results, particularly in the context of causality and partial effects measurement.

Prerequisite: Graduate standing; additional prerequisites vary with the topic and are given in the Course Schedule.

Course number may be repeated for credit when the topics vary.

Computer programming and scripting applied to geospatial data. Programming a) automates processes that are repetitive and time consuming, b) allows for the implementation of solutions that are not available as built-in functions in GIS software, c) allows for easy replication of your work and of methods, among others.

This graduate level course surveys the current and past literature in the field of land change science. The course is divided into five major parts:  part I) addresses the emergence of land change science as a fundamental component of environmental research; II) investigates the causes and drivers of land change and its theoretical foundations across certain social sciences’ disciplines; III) provides a glimpse of the land cover monitoring & characterization and modeling literature; IV) looks into the synergies and impacts of land change on other systems. Finally, part V) investigates the recent literature on forest transition, indirect land change, and land sharing/sparing and future directions of the field.

Geographic Information Systems (GIS) modeling capabilities have been used to inform and support decision making in the management of watersheds and parks, in the design of emergency evacuation plans, among others. Advanced GIS will cover a wide range of modeling applications using rasters, including watershed drainage analysis, ecological corridors and least cost path analysis. Students will also be introduced to analytical tools such as spatial data interpolation techniques, fuzzy set analysis, and location/allocation analysis. Hands-on experience will be provided through weekly labs and final project.

Geoprocessing courses can encompass a variety of topics, ranging from what we would normally simply call ‘GIS’ to remote sensing techniques to computer science. This course will adopt a narrower definition: geoprocessing will be mostly about computer scripting and programming applied to georeferenced (spatial) data. The skills you will hopefully attain in this course are useful in a number of different ways. Scripting & Programming a) automates processes that are repetitive and time consuming, b) allows for the implementation of solutions that are not available as built-in functions, c) allows for easy replication of your work and of methods, among others. 

Geographic Information Systems (GIS) modeling capabilities have been used to inform and support decision making in the management of watersheds and parks, in the design of emergency evacuation plans, among others. Advanced GIS will cover a wide range of modeling applications using rasters, including watershed drainage analysis, ecological corridors and least cost path analysis. Students will also be introduced to analytical tools such as spatial data interpolation techniques, fuzzy set analysis, and location/allocation analysis. Hands-on experience will be provided through weekly labs and final project.

This graduate level course will survey the current and past literature in the field of land change science. The course is divided into five major parts: part I) addresses the emergence of land change science as a fundamental component of environmental research; II) investigates the causes and drivers of land change and its theoretical foundations across certain social sciences’ disciplines; III) provides a glimpse of the land cover monitoring & characterization and modeling literature; IV) looks into the synergies and impacts of land change on other systems. Finally, part V) investigates the recent literature on forest transition, indirect land change, and land sharing/sparing and future directions of the field.
 

Geographic Information Systems (GIS) modeling capabilities have been used to inform and support decision making in the management of watersheds and parks, in the design of emergency evacuation plans, among others. Advanced GIS will cover a wide range of modeling applications using rasters, including watershed drainage analysis, ecological corridors and least cost path analysis. Students will also be introduced to analytical tools such as spatial data interpolation techniques, fuzzy set analysis, and location/allocation analysis. Hands-on experience will be provided through weekly labs and final project

This course will overview a wide range of statistical regression techniques applied to the social sciences and spatial contexts, including ordinary least squares, limited dependent variable models, and spatial regression models. The goal of the course is to equip geography masters and PhD students with sufficient knowledge of regression analysis to a) employ those methods in their own research, b) help them understand the empirical literature they read, and c) provide a basis for more advanced methods. Emphasis will be placed on the meaning of the assumptions needed to obtain consistent and/or unbiased estimators and cases when those assumptions may be violated. Although knowledge of college algebra and basic probability and statistics is required, the course will approach regression analysis empirically, with substantial time devoted to lab exercises, implementation of models, and interpretation of results, particularly in the context of causality and partial effects measurement.

Prerequisite: Graduate standing; additional prerequisites vary with the topic and are given in the Course Schedule.

Course number may be repeated for credit when the topics vary.

This course introduces basic concepts underlying geographic information systems and science (GIS), including related or integrated technologies and applications such as global positioning systems (GPS), cartography, and spatial analysis. It combines an overview of the general principles of GIS with a theoretical treatment of the nature and issues associated with the use of spatial environmental information. Although the course has a laboratory component that introduces students to the most commonly used GIS software package, the focus is on the “science behind the software” (eg, types and implications of functions and analysis, rather than just how to do the analysis).

This graduate level course surveys the current and past literature in the field of land change science. The course is divided into five major parts:  part I) addresses the emergence of land change science as a fundamental component of environmental research; II) investigates the causes and drivers of land change and its theoretical foundations across certain social sciences’ disciplines; III) provides a glimpse of the land cover monitoring & characterization and modeling literature; IV) looks into the synergies and impacts of land change on other systems. Finally, part V) investigates the recent literature on forest transition, indirect land change, and land sharing/sparing and future directions of the field.

Geographic Information Systems (GIS) modeling capabilities have been used to inform and support decision making in the management of watersheds and parks, in the design of emergency evacuation plans, among others. This course will cover a wide range of modeling applications using rasters, including surface hydrologic analysis, viewshed, least cost path and corridor analysis, and fuzzy set multicriteria evaluation. Students will also be introduced to analytical tools such as spatial data interpolation techniques, error assessment, and spatial pattern statistics. Hands-on experience will be provided through weekly labs and final project. This course will expand and refine GIS concepts, applications, algorithms, and analysis beyond the introductory GIS course.

This course introduces basic concepts underlying geographic information systems and science (GIS), including related or integrated technologies and applications such as global positioning systems (GPS), cartography, and spatial analysis. It combines an overview of the general principles of GIS with a theoretical treatment of the nature and issues associated with the use of spatial environmental information. Although the course has a laboratory component that introduces students to the most commonly used GIS software package, the focus is on the “science behind the software” (eg, types and implications of functions and analysis, rather than just how to do the analysis).

This graduate level course surveys the current and past literature in the field of land change science. The course is divided into five major parts:  part I) addresses the emergence of land change science as a fundamental component of environmental research; II) investigates the causes and drivers of land change and its theoretical foundations across certain social sciences’ disciplines; III) provides a glimpse of the land cover monitoring & characterization and modeling literature; IV) looks into the synergies and impacts of land change on other systems. Finally, part V) investigates the recent literature on forest transition, indirect land change, and land sharing/sparing and future directions of the field.

Geographic Information Systems (GIS) modeling capabilities have been used to inform and support decision making in the management of watersheds and parks, in the design of emergency evacuation plans, among others. This course will cover a wide range of modeling applications using rasters, including surface hydrologic analysis, viewshed, least cost path and corridor analysis, and fuzzy set multicriteria evaluation. Students will also be introduced to analytical tools such as spatial data interpolation techniques, error assessment, and spatial pattern statistics. Hands-on experience will be provided through weekly labs and final project. This course will expand and refine GIS concepts, applications, algorithms, and analysis beyond the introductory GIS course.

This course introduces basic concepts underlying geographic information systems and science (GIS), including related or integrated technologies and applications such as global positioning systems (GPS), cartography, and spatial analysis. It combines an overview of the general principles of GIS with a theoretical treatment of the nature and issues associated with the use of spatial environmental information. Although the course has a laboratory component that introduces students to the most commonly used GIS software package, the focus is on the “science behind the software” (eg, types and implications of functions and analysis, rather than just how to do the analysis).