Editing Community Data Science Workshops (Fall 2015)/Day 3 Projects/Civic data

[[File:Seattle_building_cost_heatmap.png|thumb|right|250px|What neighborhoods have changed the most since the beginning of the Seattle [http://www.seattletimes.com/pacific-nw-magazine/seattles-building-boom-is-good-news-for-a-new-generation-of-workers/ building boom]?]]

__NOTOC__
== 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 [https://data.seattle.gov 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  ([[w:Socrata|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: [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 ==
;Dataset: https://data.seattle.gov/Permitting/Building-Permits-Current/mags-97de

Run the script <pre>download_building_permit_data.py</pre>

Now open up the CSV output file to verify that you got all the data you asked for.


== Part 2: charting new construction by month ==
;Question: Has the rate of residential construction increased since 2010?

Now we want to learn whether there is, in fact, a housing boom in Seattle. We'll do this by counting how many new permits are issued each month, and then plot these on a graph.

Run the script <pre>building_permits_by_month.py</pre>

Now open up the CSV output file and check your data. Does this look right to you?

;Challenge question: how could we separate out single family homes from apartments, and count/plot them separately?


== Part 3: charting new construction by neighborhood ==
;Question: which Seattle neighborhoods have had the most multifamily residential construction (apartments and townhomes) since 2010?

Now we want to learn where all this new construction is happening. We'll do this by sending the address for each MULTIFAMILY permit to the Google Geolocation API, which will return the neighborhood where that address is located.

Run the script <pre>building_permits_by_neighborhood.py</pre>

Now open up the CSV output file and check your data. Does this look right to you?

;Challenge question: In which Seattle neighborhood is the cost of new apartment construction projects highest, on average?

== Part 4: Mapping new home construction in Seattle ==
;Question: what locations have experienced the highest density of new construction since 2010?

Now we will try to get an even more detailed picture of where this construction is occuring, using Google Fusion tables, a powerful visualization application that makes it easy to plot data on a map.

Go [https://support.google.com/fusiontables/answer/2571232?hl=en here] and follow the steps to upload your '''new_all_2010-2015.csv''' file. 

*Create a point map and heatmap.
*Experiment with filtering and weighting the points on your map. 

;Example map: https://www.google.com/fusiontables/data?docid=1gm0wVqnK7zQ7hgp5bsKoBIetMZ75jo-VMs5noDoJ#map:id=3

== Going further ==
''Try to answer these additional questions that draw on the data and methods we're learning today. Ask a mentor if you get stuck!''
#Which developer has spent the most on new construction in Seattle since 2010?
#How many townhouses have been constructed in Seattle since 2010?
#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 [https://data.seattle.gov/Permitting/Building-Permits-Older-than-5-years/47eb-r92t the previous 5 years]?



== Resources ==



[[Category:Fall_2015_series]]
@@ Line 20: / Line 20: @@
-== Part 1: Downloads ==
+== Preparation ==
+;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]]''
-;Step 1: Download today's scripts: [http://jtmorgan.net/cdsw/nov7cdsw.zip Python scripts]
+== Part 1: getting the building permit data ==
+;Dataset: https://data.seattle.gov/Permitting/Building-Permits-Current/mags-97de
-;Step 2: Get the dataset: Unzip the folder, navigate to it in your terminal, and run this script <pre>download_building_permit_data.py</pre>
+Run the script <pre>download_building_permit_data.py</pre>
 Now open up the CSV output file to verify that you got all the data you asked for.
-You can view the full set of permit applications at: https://data.seattle.gov/Permitting/Building-Permits-Current/mags-97de
-;Challenge question: how could we change our API query to download the applicant names for all COMMERCIAL building permits issued within the past year?
 == Part 2: charting new construction by month ==
@@ Line 38: / Line 40: @@
 Now we want to learn whether there is, in fact, a housing boom in Seattle. We'll do this by counting how many new permits are issued each month, and then plot these on a graph.
-Run the script <pre>residential_permits_by_month.py</pre>
+Run the script <pre>building_permits_by_month.py</pre>
 Now open up the CSV output file and check your data. Does this look right to you?
 ;Challenge question: how could we separate out single family homes from apartments, and count/plot them separately?
 == Part 3: charting new construction by neighborhood ==
@@ Line 49: / Line 52: @@
 Now we want to learn where all this new construction is happening. We'll do this by sending the address for each MULTIFAMILY permit to the Google Geolocation API, which will return the neighborhood where that address is located.
-Run the script <pre>multifamily_permits_by_neighborhood.py</pre>
+Run the script <pre>building_permits_by_neighborhood.py</pre>
 Now open up the CSV output file and check your data. Does this look right to you?
@@ Line 78: / Line 81: @@
 == Resources ==
-;Press about the construction boom
-*http://www.theurbanist.org/2015/10/20/fact-check-no-explosion-in-demolitions/
-*http://arcadenw.org/article/changing-seattle
-*http://www.seattlemag.com/article/demolitions-seattle-no-neighborhood-unaffected
-*http://www.seattletimes.com/pacific-nw-magazine/seattles-building-boom-is-good-news-for-a-new-generation-of-workers/
-=== Datasets ===
-* Permits, last 5 years (today's dataset): https://data.seattle.gov/Permitting/Building-Permits-Current/mags-97de
-* Permits older than 5 years: https://data.seattle.gov/Permitting/Building-Permits-Older-than-5-years/47eb-r92t
-;Custom Socrata datasets
-multifamily 2010-2015: https://data.seattle.gov/Permitting/Building-permits-new-multifamily-residential-const/ma3y-m69a
-single and multifamily 2010-2015: https://data.seattle.gov/Permitting/Building-permits-new-residential-construction/kdfe-reh3
-=== Sample visualizations ===
-*Building permit charts: https://docs.google.com/spreadsheets/d/15DBcWnCroga4B1_ss66YjW9hlJxEk3g1UK7khFLpbQM/edit#gid=0
-*Building permit Fusion table: https://www.google.com/fusiontables/DataSource?docid=1gm0wVqnK7zQ7hgp5bsKoBIetMZ75jo-VMs5noDoJ#rows:id=1
-=== APIs ===
-* Hurl.it API testing tool: https://www.hurl.it
-;Sample API queries
-* Building permit data: https://data.seattle.gov/resource/mags-97de.json?$where=issue_date%20IS%20NOT%20NULL%20&action_type=NEW&category=MULTIFAMILY&$limit=100
-* Google maps location (using street address): http://maps.googleapis.com/maps/api/geocode/json?address=5601+22ND+AVE+NW+Seattle+WA
-* Google maps location (using lat/long): http://maps.googleapis.com/maps/api/geocode/json?latlng=47.66979666%2C-122.38570052
-=== Google tools ===
-*Fusion tables: https://support.google.com/fusiontables/answer/2571232
-*About Fusion table heatmaps: https://support.google.com/fusiontables/answer/1152262
-*About Google maps geocoding API: https://developers.google.com/maps/documentation/geocoding/
-=== Socrata open data portal===
-*More about the Socrata open data portal and API: http://www.socrata.com/products/open-data-portal/
-* Socrata API help resources: http://dev.socrata.com/consumers/getting-started.html
-:* filtering results: http://dev.socrata.com/docs/filtering.html
-:* dealing with timestamps: http://dev.socrata.com/docs/datatypes/timestamp.html
-:* writing API queries: http://dev.socrata.com/docs/queries.html
-;Data portal-powered apps
-*https://www.seattleinprogress.com/
-*http://web6.seattle.gov/mnm/
-;Socrata API instructional videos
-* https://data.seattle.gov/videos
-* https://www.youtube.com/watch?v=YlKzXTrTLOQ
-* https://www.youtube.com/watch?v=Whfp8ojMf0U
-* https://www.youtube.com/watch?v=Vd6bwz3ivVA
-;Some other government website that use the Socrata API
-* https://data.austintexas.gov/
-* https://data.cityofchicago.org/
-* https://data.cityofnewyork.us/
 [[Category:Fall_2015_series]]