Geography Track

Introduction to Quantitative Methods is an introductory course in applied statistics with an emphasis on computer skills. Students gain proficiency in using spreadsheets to organize data and to perform the most common statistical methods such as univariate analysis, hypothesis testing, estimation of means, regression, and categorical association. Geography majors receive credit for a skills course. Environmental Science majors receive credit for a statistics course.

Prerequisites: Prerequisites are high school math such as Algebra 2 and/or pre-calculus.

Course Designation/Attribute: FA

Anticipated Terms Offered: Every Semester

This course introduces Geographic Information Science (GIS) as a powerful mapping and analytical tool. Topics include GISc data structure, map projections, and fundamental GISc techniques for spatial analysis. Laboratory exercises concentrate on applying concepts presented in lectures and incorporate two widely used GISc software packages – IDRISI (created by Clarklabs) and ArcGIS (created by ESRI). These exercises include examples of GISc applications in environmental modeling, socio-demographic change and site suitability analyses. Although the course is computer-intensive, no programming background is required. A formal-analysis course. Counts as skills course or core course in mapping sciences/spatial analysis in geography major.

Course Designation/Attribute: FA

Anticipated Terms Offered: Offered every semester

Free and open source R is increasingly used for geospatial analyses. R and its ecosystem of supporting software also facilitate the creation, presentation, and reproducibility of analyses. R is therefore very close to being a one-stop shop for the modern GIScientist. This course will provide students with the skills they need to use R as a GIS. There will be additional emphases on programming, presentation, and reproducibility, which will entail learning to develop R libraries, development of presentations and reports using Rmarkdown, and using version control with github. Students will learn and apply R skills by working on a specific research problem. Students should have prior programming experience. Open to upper level undergraduate and graduate students. Satisfies the Skills requirement or can count as a specialization course in GIS in the undergraduate geography and global environmental studies major/minor.

Prerequisites: GEOG 190

Anticipated Terms Offered: annually

Intermediate Quantitative Methods in Geography extends the concepts of introductory statistics to multivariate regression, principal components, spatial statistics, and additional intermediate methods. The course uses the Statistical Package for the Social Sciences (SPSS) software, which works as a spreadsheet with dropdown menus. Students learn how to select a method and interpret its output in a practical manner that avoids common misconceptions. Students apply the concepts to group projects based on student interest.

Prerequisites: Introductory statistics course, such as GEOG 110 / GEOG 311  Intro to Quantitative Methods, or high school Advanced Placement Statistics

Anticipated Terms Offered: Offered every spring

GIS & Land Change Models examines computerized models that simulate land change. Such models are important because land change influences socioeconomic development, biodiversity conservation, water resources, energy use, greenhouse gasses, and many other factors. Examples are in Massachusetts to extrapolate suburbanization and in the tropics to Reduce Emissions due to Deforestation and Degradation (REDD). Students learn fundamental concepts such as calibration, extrapolation, validation, and sensitivity, along with technical aspects of TerrSet’s Land Change Modeler and Geomod. Students apply the concepts in group projects.

Prerequisites: Prerequisite is Intro to GIS, listed as GEOG 190 or SSJ 310.

Course Designation/Attribute: POP

Anticipated Terms Offered: Every fall

GIS & Map Comparison investigates metrics that scientists use and abuse focusing on applications to Remote Sensing and Geographic Information Science with raster data. Methods compare two variables of the same phenomenon, such as comparison of an initial time versus a subsequent time, or comparison of predictions versus observations. Students learn how to compute and to interpret metrics such as Hits, Misses, False Alarms, Mean Deviation, Mean Absolute Deviation, Area Under the Total Operating Characteristic curve, among others. Students apply and develop the concepts in group projects. The professor wrote the book, literally.

Prerequisites: Prerequisite is Intro to GIS, listed as GEOG 190 or SSJ 310.

Course Designation/Attribute: FA, POP

Anticipated Terms Offered: Every fall

Application of remote sensor systems in earth science and other disciplines; interpretation of multispectral scanner, RADAR and thermal imagery, classification, postclassification analysis, special transformations, multitemporal data analysis for change detection, the study of spectral characteristics of vegetation, soils, water, minerals and other materials. The specific objectives of the course are to acquaint the student with the physical principles underlying remote sensing systems and the primary remote-sensing data-collection systems; introduce the student to methods of interpreting and analyzing remotely sensed data; provide some insight concerning the applications of remote sensing in various discipline areas; and provide hands-on experience in digital image processing using software packages available in the computer lab.

Anticipated Terms Offered: Offered every year

Understanding the Earth System depends on observing observations of socioeconomic and environmental patterns and processes across multiple spatial and temporal scales. These scales span seconds to decades in time, and centimeters to millions of square kilometers in space. Earth Observation (EO, also known as remote sensing) is the only feasible means for providing this range of perspectives, but our ability to collect data across all necessary scales is currently limited by inherent tradeoffs between the extent, duration, frequency, and resolution of observation. This suggests the possibility that there may be important, but currently unknown, phenomena that exist within our observational blind spots. Some of this blindness is imposed by physics (there are only so many photons reflected from the Earth, and these are proportional to wavelength), but many are due to engineering or economic constraints (some sensors are too expensive to use more than once or over a large area). These latter hurdles are falling, however, as new “big data” analytical techniques emerge, and combine with increasingly available, high quality, low-cost data made possible by a host of new innovations, including cheap satellites, unmanned aerial systems, inexpensive cellphone enabled field sensors, and the availability of a large pool of internet-enabled workers who can interpret these data in ways that computers cannot. This course provides students hands-on experience working with these new EO technologies.

Prerequisites: GEOG 293

Basic programming experience required.

Laptop required

Anticipated Terms Offered: Bi-annually

This course is designed to introduce the students to the principles and analytical methods of satellite remote sensing as applied to environmental systems (e.g., land-cover classification, vegetation monitoring, etc.). Lectures will cover principles of remote sensing, sensor types, as well as the processing and analysis of multispectral satellite images (e.g. Landsat and SPOT). A series of hands-on lab exercises will complement students’ understanding of lecture material and also helps students to become familiar with image processing functions of the IDRISI image analysis software. Particular emphasis will be placed on final group project that brings a real world perspective to the learning process.

Prerequisites: Vector GIS or Introduction to GIS, and must register for Lab. Introduction to GISc and Introduction to Quantitative Methods desirable.

Anticipated Terms Offered: Offered every year.

This course builds on Introduction to GIS by delving deeper into raster GIS. Topics include time-series analysis, uncertainty assessment, multi-objective decision making, land-change modeling, and spatial statistics. Concepts in lectures are illustrated using the Idrisi software. Final project is required. This is a prerequisite for the fifth year Masters program in GIS and is a requirement for the GISDE masters program. Open to Seniors (Juniors by permission).

Prerequisites: GEOG 190 OR SSJ 310 OR GEOG 293 OR GEOG 383.

Anticipated Terms Offered: Offered every spring