All posts filed under: Coursera

Analyzing Density Bonus Developments in the City of Los Angeles

On February 22, 2016, I started the GIS Specialization Course with UC Davis through Coursera. For those of you who have paid attention, I have started the final course of the specialization: Geospatial Analysis Project. As with other Coursera specializations, this is a Capstone project that is the culmination of the previous courses. For this project, I have to propose, design, analyze, and present a geospatial analysis project from start to finish. This week requires the creation of my project proposal, which is as follows (if any of you have suggestions on data sources and/or analysis, please feel free to comment): What is Density Bonus? Density Bonus is a program through which a developer can apply for a project with a unit density greater than that allowed by the current land use zoning, as calculated from unit floor area and floor area ratio (FAR). In exchange for the higher density, the developer must set aside a certain number of units to be affordable: this is by restricting the rent levels or sale prices to targeted income levels …

Data Analysis and Interpretation Capstone

So, this is the end. It took six months, but today I completed and was certified for the Data Analysis and Interpretation Specialization by Wesleyan University through Coursera. When I first started in October 2015, I had no idea how to write code in Python, let alone produce graphs and run statistical analysis. It has been a fun experience learning how to write code in Python and learning the different kinds of statistical methods. Ironically, I learned these after I left graduate school. One would think that these are method courses you would take in school. For the Capstone Project, I do wish the data was more complete and over a longer period of time. It is difficult to run analysis on data that only goes back as far as 1972 and in many cases, missing records for many years in between. The results can be quite misleading, as it pointed to fertility rate as being highly correlated with environmental sustainability. However, fertility rate, in many cases is contingent on many different factors that are both quantitative …

Capstone Project: Results

For those following my blog on my Data Analysis and Interpretation Specialization by Wesleyan University through Coursera, this is the final course and the Capstone project. Unlike previous courses, I will move away from urbanization data and try to tackle one of the problems provided by the course’s industry partner. This is my introduction. Below is our third assignment – the preliminary results. Results Only the results for Burundi, Ethiopia, and Liberia will be reported, as the other countries demonstrated no change or very slight change in the ensure environmental sustainability index. Descriptive Statistics: The following table shows the descriptive statistics for the Ensure Environmental Sustainability Index for each of the selected countries, starting from the lowest GDP per capita group to the highest. The standard deviations are much greater for the lowest GDP per capita group compared to the others. In three countries, Seychelles, Canada, and Ireland, no change in the value of the index was observed. It would appear that countries that reach a certain GDP per capita will have achieved a mean Ensure …

GIS Specialization – GIS Data Formats, Design and Quality

On February 22, 2016, I started the GIS Specialization Course with UC Davis through Coursera. Today, I completed the second course, GIS Data Formats, Design and Quality, in the series. For my second assignment, I was given parcel data and a raster containing elevation information on the town of Valmeyer. The entire town was moved to a location on a higher elevation in order to minimize flood risks. From the data, I calculated average distance from the new parcels to the old town. I also calculated slope and elevation for each of the new parcels (min, max, and mean values). This required the use of Spatial Analyst, which is a package that is new to me. Prior to this, I had not worked with raster data so it was a great learning experience. After completing the calculations and adding them to the attribute data, I created and uploaded the features as shapefiles to create a publicly accessible web map on ArcGIS Online. This is my map: Honestly, I love cartography and spatial analysis. I look …

Capstone Project: Methods

For those following my blog on my Data Analysis and Interpretation Specialization by Wesleyan University through Coursera, this is the final course and the Capstone project. Unlike previous courses, I will move away from urbanization data and try to tackle one of the problems provided by the course’s industry partner. This is my introduction. Below is our second assignment – the data management and analysis methods. Methods Sample: Out of the 211 World Bank recognized sovereignties, 8 (N=8) were chosen for this study. Countries that has the Ensure Environmental Sustainability goal were selected: three countries with the lowest GDP per capita (Burundi, Ethiopia, Liberia), three countries with the highest GDP per capita (Canada, Ireland, United States), and two from the median (Estonia, Seychelles). In addition to identifying associations between variables and the four sustainability indicators, this selection was used to also investigate how variable relationships differ in countries with varying degrees of economic development. Each country, depending on available data, has between 26 to 43 indicators for analysis with 36 years of data from 1972 to …

Capstone: Variables Associated With Environmental Sustainability – A United Nations Millennium Development Goal

For those following my blog since the start of my Data Analysis and Interpretation Specialization by Wesleyan University through Coursera, this is the final course and the Capstone project. Unlike previous courses, I will move away from urbanization data and try to tackle one of the problems provided by the course’s industry partner. Below is our first assignment – the introduction to my final report. Variables Associated with Environmental Sustainability Using data provided by the World Bank, through DrivenData, this study looks to identify factors associated with the Environmental Sustainability Indicator defined as an United Nations Millennium Development Goal (MDG). Preliminary explanatory variables are Gross National Income, Forest Area, CO2 Emissions, Employment, Foreign Direct Investments, Household Final Consumption Expenditure, Adult Literacy Rate, Urban Population, Investments in Energy, and Energy Use. This mix of both economic and social factors will be examined for associations with the UN-MDG indicator of environmental sustainability. After the associated variables are identified, they will be used to create a model to predict data for the years 2008 and 2012. As a social/urban scientist interested …

GIS Specialization – Fundamentals of GIS

On February 22, 2016, I started the GIS Specialization Course with UC Davis through Coursera. Today, I completed the first course in the series. As I already have a couple years of GIS experience, the first course Fundamentals of GIS was more like a review of the basics. At the same time, I definitely learned new skills such as map package sharing and creating bookmarks. To complete the first course, I needed to create a map for the final assignment. The original data is at the precinct level. I had to aggregate the voting data for Proposition 37 and total votes to the county level. This is the map I created: I must say, this process took longer than I expected. I am definitely a bit rusty with the map-making. My spatial analysis skills are also rusty. At first, I used a different geoprocessing tool. Instead of directly using a spatial join with the intersect method, I took the long way around using Intersect, Merge, and then Dissolve. However, this presented issues because the data became more …