Community Data Science Workshops (Fall 2015)/Day 3 Projects/Civic data: Difference between revisions
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We will also explore an important non-technical part of data science: thinking critically about data. Thinking critically about the data you have, and what conclusions you can draw from it, is especially important when you are visualizing data, because its easy to mislead people with visualizations. We'll discuss how visualizations based on incorrect data can lead people to make false conclusions, using an example from a recent visualization of building demolitions published in the Northwest design magazine ''Arcade''. | We will also explore an important non-technical part of data science: thinking critically about data. Thinking critically about the data you have, and what conclusions you can draw from it, is especially important when you are visualizing data, because its easy to mislead people with visualizations. We'll discuss how visualizations based on incorrect data can lead people to make false conclusions, using an example from a recent visualization of building demolitions published in the Northwest design magazine ''Arcade''. | ||
== Preparation == | == Preparation == | ||
;Step 1: Download today's scripts | ;Step 1: Download today's scripts: [http://jtmorgan.net/cdsw/nov7cdsw_scripts.zip Python scripts] | ||
; | ;Step 2: Download today's datasets: [http://jtmorgan.net/cdsw/nov7cdsw_data.zip datasets] | ||
''If you are confused by anything today, go back and refresh your memory with the [[Community Data Science Workshops (Fall 2015)/Day 0 setup and tutorial|Day 0 setup and tutorial]] and [[Community Data Science Workshops (Fall 2015)/Day 0 tutorial|Day 0 tutorial]]'' | |||
== | == Part 1: getting the building permit data == | ||
;Question: Has the rate of apartment construction increased since 2010? | ;Question: Has the rate of apartment construction increased since 2010? | ||
;Today's dataset: [https://data.seattle.gov/Permitting/Building-Permits-Current/mags-97de Click here to view the Seattle Building Permits database] | |||
== Part 2: new construction by month == | |||
== Part 3: new construction by neighborhood == | |||
== Visualizing new home construction in Seattle by neighborhood == | == Visualizing new home construction in Seattle by neighborhood == | ||
;Question: which Seattle neighborhoods have had the most multifamily residential construction (apartments and townhomes) since 2010? | ;Question: which Seattle neighborhoods have had the most multifamily residential construction (apartments and townhomes) since 2010? | ||
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== Resources == | |||
[[Category:Fall_2015_series]] | [[Category:Fall_2015_series]] | ||
Revision as of 23:41, 6 November 2015
What neighborhoods have changed the most since the beginning of the Seattle building boom?
Building and visualizing datasets using data.seattle.gov and Google apps
In this project, we will explore a few ways to analyze and visualize data from data.seattle.gov. We will examine trends in the construction of houses, townhomes, and condos in Seattle over the last five years, in order to determine the true extent of the current "construction boom".
- We'll download data from two APIs (Socrata open data platform and Google Maps)
- We'll analyze trends over time in our dataset
- We'll visualize our data in graphs and heatmaps
Topics we will cover
- Writing and testing complex API queries
- Reading and writing CSV and JSON files
- Filtering and aggregating data
- Combining data from multiple APIs
- Graphing and mapping data
We will also explore an important non-technical part of data science: thinking critically about data. Thinking critically about the data you have, and what conclusions you can draw from it, is especially important when you are visualizing data, because its easy to mislead people with visualizations. We'll discuss how visualizations based on incorrect data can lead people to make false conclusions, using an example from a recent visualization of building demolitions published in the Northwest design magazine Arcade.
Preparation
- Step 1
- Download today's scripts: Python scripts
- Step 2
- Download today's datasets: datasets
If you are confused by anything today, go back and refresh your memory with the Day 0 setup and tutorial and Day 0 tutorial
Part 1: getting the building permit data
- Question
- Has the rate of apartment construction increased since 2010?
- Today's dataset
- Click here to view the Seattle Building Permits database
Part 2: new construction by month
Part 3: new construction by neighborhood
Visualizing new home construction in Seattle by neighborhood
- Question
- which Seattle neighborhoods have had the most multifamily residential construction (apartments and townhomes) since 2010?
Mapping new home construction in Seattle
- Question
- what locations have experienced the highest density of new construction since 2010?
Challenge questions
Try to answer these additional questions that draw on the data and methods we're learning today. Ask a mentor if you get stuck!
- In which Seattle neighborhood is the cost of new construction projects highest, on average?
- Where in Seattle are the most commercial buildings being constructed in 2015?
- How does the rate of residential construction in Seattle from 2010-2015 compare to the previous 5 years?
- How many townhouses have been constructed in Seattle since 2011?
