Final Project: Flood Plain Analysis on the Middlebury River
In this final project, I chose to build upon the SAGA stream analysis performed in lab 4. The goal for this project was to determine the elevation of flood plains relative to the surface of the river and compare those elevation-based flood plains to FEMA’s flood plain maps. This analysis was implemented on the Middlebury River from where the river exits the Green mountains at East Middlebury and where it joins Otter Creek.
Big Data and Twitter Analysis Lab 10
An analysis of twitter data on Hurricane Dorian.
Malawi Resilience Labs 7&8
Reproducibility of a Malawi physical exposure resilience study. See the results here.
Twitter Data ‘use case’
A review of the reproducability and replicability of “Who Tweets with Their Location? Understanding the Relationship between Demographic Characteristics and the Use of Geoservices and Geotagging on Twitter” by Luke Sloan and Jeffery Morgan
SQL Lab
In our recent SQL Lab we used drain and wetland data from Ramani Huria to visualize drain density per subward and the intersection of subwards with wetlands. We used SQL queries to analyze and visualize the data. This lab is presented as a potential lesson plan/tutorial for other students.
Lab 4 - Model Error Propagation and Uncertainty
10/9/2019
In Lab 4 I preformed a hydrological analysis of Mount Kilimanjaro with both Aster and SRTM data. The hydrological analysis was preformed with these two data types in order to compare the two results and find the preferable data source for this use.
Lab 3 - Global Digital Elevation Models
9/26/2019
In Lab 3 we worked with SAGA to create a model of streams and rivers based off of an Aster DEM.
Lab 2 - First Model Revised!
This model measures the distance, direction (degrees), and orientation (NSEW) of a polygon(s) from a central point or polygon.
Lab 1 - First Model!
In our first lab we made a model that measures the distance and direction from a point to the centroid of a polygon. Download My First Model.
Assignment 1
9/11/2019
A review of the article “Open Network for Local Self Sustainability, Boosting Bioregional Development Through an Open Data Sharing System” taken from the FOSS4G 2019 conference.
This article explains a developing geodatabase called Open NETwork for Local Self Sustainability. The purpose of the database is to suggest a way to create the most self-sustainable communities, especially focusing on ways to make future community development take into account sustainability in their development planning. The database is modeled on Italian communities and focuses primarily on the scale of municipalities. The geodatabase aims to create self-sustainable communities by finding the most sustainable supply chain routes to a community for products that fall in the categories of housing, food, transport and waste. The indicators used to measure the self-sustainability of a supply chain for a product are the amounts of non-renewable primary energy, renewable primary energy, amount of productive land, and local manpower the chain uses. The database uses the opensource programs GRASS and QGIS to create the suggested sustainable supply chains. In the future, the database hopes to expand the types of supply chains that are included by drawing in different fields of research.
To me, the open source nature of this project is extremely important to achieving this database’s goal. Being created on the open source programs QGIS and GRASS allow almost anyone to have access to the program, which is key in not only disseminating the program to city planners but also allowing people from different areas of research to contribute their supply chain data to the geodatabase. The Open NETwork for Local Self Sustainability necessitates different groups working together to achieve sustainability goals.