Advanced study of geographic information systems (GIS). Topics include spatial data models, topology, data encoding, data quality, database management, spatial analysis tools and visualization techniques. Hands-on experience using an advanced vector GIS package. prereq: B or better in 3561 or 5561 or instr consent

Introduction to study of geographic information systems (GIS) for geography and non-geography students. Topics include GIS application domains, data models and sources, analysis methods and output techniques. Lectures, readings and hands-on experience with GIS software. prereq: Jr or sr

Spatial data development/analysis in science/management of natural resources. Data structures/sources/collection/quality. Geodesy, map projections, spatial/tabular data analysis. Digital terrain analysis, cartographic modeling, modeling perspectives, limits of technology. Lab exercises. Both onsite and fully online options for course enrollment. prereq: Soph or jr or sr or UHP fr

GPS principles, operations, techniques to improve accuracy. Datum, projections, and coordinate systems. Differential correction, accuracy assessments discussed/applied in lab exercises. Code/carrier phase GPS used in exercises. GPS handheld units, PDA based ArcPad/GPS equipment. Transferring field data to/from desktop systems, integrating GPS data with GIS. prereq: Intro GIS course

Application of geographic information science and technologies (GIS) in complex environmental problems. Students gain experience in spatial data collection, database development, and spatial analysis, including GNSS and field attribute collection, image interpretation, and existing data fusion, raster/vector data integration and analysis, information extraction from LiDAR data, DEM conditioning and hydrologic analysis, neighborhood analysis, bulk processing and automation, and scripting. Problems vary depending on topics, often with extra-University partners. prereq: FNRM 3131 or Geog 3561 or instr consent

Introductory principles and techniques of remote sensing and geospatial analysis applied to mapping and monitoring land and water resources from local to global scales. Examples of applications include: Land cover mapping and change detection, forest and natural resource inventory, water quality monitoring, and global change analysis. The lab provides hands-on experience working with satellite, aircraft, and drone imagery, and image processing methods and software. Prior coursework in Geographic Information Systems and introductory Statistics is recommended. Prereq: None, but prior coursework in GIS and Statistics is recommended.

This course builds on the introductory remote sensing class, FNRM 3262/5262. It provides a detailed treatment of advanced remote sensing and geospatial theory and methods including Object-Based Image Analysis (OBIA), lidar processing and derivatives, advanced classification algorithms (including Random Forest, Neural Networks, Support Vector Machines), biophysics of remote sensing, measurements and sensors, data transforms, data fusion, multi-temporal analysis, and empirical modeling. In-class and independent lab activities will be used to apply the course topics to real-world problems. Prior coursework in Geographic Information Systems, remote sensing, and statistics is necessary. Prereq: grad student or instr consent

First of a two-course series focusing on ArcGIS Desktop. Overview of ArcGIS system and its use for spatial data processing. Data capture, editing, geometric transformations, map projections, topology, Python scripting, and map production. prereq: [GEOG 5561 or equiv, status in MGIS program, familiarity with computer operating systems] or instr consent

Continues GIS 5571. Raster analysis, dynamic segmentation, geometric networks, geocoding, Python scripting, and data interoperability. Substantial projects include map and poster design and production. prereq: [5571, [GEOG 5561 or equiv], in MGIS program] or instr consent

Soil, landscape, and crop spatial variability. GIS, DEM, GPS technologies. Variable rate machinery, PA software, remote sensing. Geostatistics, sampling, experimental designs. Precision integrated crop management. Data acquisition, processing, and management. Socio-economical and e-marketing aspects. prereq: Basic sciences, statistics, soil, agronomy

Rigorous analysis of algorithms/implementation. Algorithm analysis, sorting algorithms, binary trees, heaps, priority queues, heapsort, balanced binary search trees, AVL trees, hash tables and hashing, graphs, graph traversal, single source shortest path, minimum cost spanning trees. prereq: [(1913 or 1933) and 2011] or instr consent; cannot be taken for grad CSci cr

Concepts, conceptual data models, case studies, common data manipulation languages, logical data models, database design, facilities for database security/integrity, applications. prereq: 4041 or instr consent

Theory, design, programming, and evaluation of interactive application interfaces. Human capabilities and limitations, interface design and engineering, prototyping and interface construction, interface evaluation, and topics such as data visualization and World Wide Web. Course is built around a group project. prereq: 4041 or instr consent

GEOG 3511/5511 is a basic introduction to cartography?the art, science, and technology of maps and map making. Our primary emphasis will be on map making, with lesser emphasis on cartographic research and the history of cartography. Lectures will focus on modern cartographic design principles, how they were developed, and how they might be changing. Lab assignments help develop skills using digital tools for producing effective maps. The course has several specific learning objectives: ? use software to create maps that communicate their subjects appropriately and effectively using sound cartographic design principles ? acquire or produce a base map that is appropriate in scale, projection, and generalization ? select and aggregate data appropriately to represent on a map using a suitable symbolization strategy ? gain an understanding of how current changes in technology impact maps and map making ? understand how fundamental design decisions might differ for printed maps and web/mobile maps ? understand how contemporary GIS and cartography are interrelated, including the use of GIS becoming ubiquitous and map making becoming increasingly available to anyone ? gain an appreciation for the 3,500+ year history of maps! prereq: 3 cr in geog or instr consent

"Everything is related to everything else, but near things are more related than distant things." The First Law of Geography proposed by Waldo Tobler implies the complex yet fascinating nature of the geospatial world. Spatial analysis in order to understand geographic numbers is becoming increasingly necessary to support knowledge discovery and decision-making. The objective of this course is to teach the fundamental theory and quantitative methods within the scope of geospatial analysis. The course starts with basic statistics, matrix, the background of spatial analysis, and exploratory spatial data analysis. Then, we will dive into the special nature of our spatial world, with fundamental geographic ideas and theories being introduced. The focus will be on numerical methods and models including descriptive statistics, pattern analysis, interpolation, and regression models. Finally, some advanced topics regarding spatial complexities and spatial networks will be introduced to arouse further interest in this realm. To sum, this is an introductory course that makes use of quantitative analytics such as linear algebra, statistics, and econometrics for spatial data analysis. By taking this course you will: -quantitatively understand critical concepts behind geospatial processes, such as scale, spatial weights, spatial autocorrelation, spatial dependence, spatial pattern. -learn key methods of analyzing spatial data: e.g., point pattern analysis, spatial autocorrelation statistics, spatial prediction, and spatial regression. -examine the lectured methods/models with data from geographic scenarios using Python and related programming packages. (Prereq: high-school algebra; Basic stats and linear algebra recommended)

Core concepts in urban geographic information science including sources for urban geographical and attribute data (including census data), urban data structures (focusing on the TIGER data structure), urban spatial analyses (including location-allocation models), geodemographic analysis, network analysis, and the display of urban data. prereq: 3561 or 5561