This unit was developed for a junior level pre-Calculus class to be taught during the first quarter of the 2016-17 school year. The lessons of the unit will culminate in each group of students creating and analyzing a mathematical model to predict the future impacts of climate change in New Hampshire and make a presentation as a group. The texts and historic data source, while specific to New Hampshire, may be of interest to other regions of the country. However, state climate change reports and climate data specific to your location may be available through state universities and meteorological stations.
This course introduces the basic techniques of demographic analysis. Students will become familiar with the sources of data available for demographic research. Population composition and change measures will be presented. Measures of mortality, fertility, marriage and migration levels and patterns will be defined. Life table, standardization and population projection techniques will also be explored.
Mapping mangroves is a project dedicated to preservation and understanding of the world's mangrove forests. Through the use of Ushahidi, an open source project that allows for users to crowdsource data, participants will report their findings.
Data modeling activity using oil reserve and consumption data. Students predict when oil reserves meet or exceed reserves.
- Material Type:
- Science Education Resource Center (SERC) at Carleton College
- Provider Set:
- Process of Science
- Steve Iona
- Date Added:
A spatial database is the backbone of a successful organization or website that depends upon maintaining and using data pertinent to locations on Earth. In GEOG 868, Spatial Database Management, capabilities specific to Relational Database Management Systems (RDBMS) and Geographic Information Systems (GIS) are combined to teach students to create, maintain, and query spatial databases in both desktop and enterprise environments. Learn the basics of Standard Query Language (SQL) and database design/normalization, the specifics of managing spatial data in an open-source technologies context (Postgres/PostGIS) and in the context of the Esri geodatabase. Along the way, you will become familiar with spatial functions and versioning, the latter in a server environment hosted by Amazon Web Services.
Students act as food science engineers as they explore and apply their understanding of cooling rate and specific heat capacity by completing two separate, but interconnected, tasks. In Part 1, student groups conduct an experiment to explore the cooling rate of a cup of hot chocolate. They collect and graph data to create a mathematical model that represents the cooling rate, and use an exponential decay regression to determine how long a person should wait to drink the cup of hot chocolate at an optimal temperature. In Part 2, students investigate the specific heat capacity of the hot chocolate. They determine how much energy is needed to heat the hot chocolate to an optimal temperature after it has cooled to room temperature. Two activity-guiding worksheets are included.