Maps, Lies and Storytelling | P06 & P07| SQL & CartoCSS

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Maps, Lies & Storytelling Class 4

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Welcome!

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homework

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Maps, Lies & Storytelling Section 6 - Learning SQL

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SQL in CartoDB Can be viewed as the entry point for complex geospatial analysis, tool building, and data manipulation

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Common SQL Statements SELECT UPDATE INSERT DELETE * there are lots of that we wont get into ** these can (and often are) mixed

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Breaking down a SELECT statement

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SELECT * FROM us_states The FROM statement tells us which table we are selecting data from

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SELECT * FROM us_states The “*” here means, select ALL ﬁelds (or columns)

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SELECT cartodb_id, name FROM us_states Here, I’ve opted to select only two columns speciﬁcally

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SELECT cartodb_id, name FROM us_states The result of this query will have just the two columns I ask for, but for every row in my dataset cartodb_id name 1 new york 2 alabama etc

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Here, I can limit it to just one column. SELECT cartodb_id FROM us_states cartodb_id 1 2 etc Notice that the result contains the column name

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You can create an ‘alias’ for the result. Basically, change the name it uses for the result. SELECT cartodb_id AS my_id FROM us_states my_id 1 2 etc Notice that the results are the same, just a diﬀerent name

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You can modify values on the ﬂy using SQL, for example, simple arithmetic. SELECT cartodb_id + 4 AS id FROM us_states id 5 6 etc The result isn’t stored anywhere permanently, it is derived on the ﬂy

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You can use a type of function, called an ‘Aggregate’ that measures across all rows at once. SELECT min(cartodb_id) AS id FROM us_states id 1 Now, only 1 row is returned

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You can use a type of function, called an ‘Aggregate’ that measures across all rows at once. SELECT sum(cartodb_id) FROM us_states sum 1328 Now, only 1 row is returned

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You can make up columns out of thin air. ! Notice that andrew is single quoted. That is the standard for strings SELECT cartodb_id, ‘andrew’ FROM us_states notice that ‘andrew’ remains constant for all rows cartodb_id unknown 1 andrew 2 andrew etc

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Filtering using WHERE

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Using WHERE statements will allow you to narrow in on exactly the rows you want SELECT cartodb_id FROM us_states WHERE cartodb_id = 1 cartodb_id 1 Here, only the single matching row is returned

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Using WHERE statements work on all types of data SELECT name FROM us_states WHERE name = ‘New York’ name New York See!

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When using string columns, they case sensitive SELECT name FROM us_states WHERE name = ‘new york’ name oh no! no results

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Using WHERE statements will allow you to narrow in on exactly the rows you want SELECT cartodb_id FROM us_states WHERE cartodb_id > 24 cartodb_id 25 26 etc

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You aren’t limited to using values as they exist, you can manipulate them on the ﬂy SELECT cartodb_id FROM us_states WHERE cartodb_id*4=20 cartodb_id 5

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With strings, you can do things like case insensitive partial matching. Here, ‘%’ is a wildcard, and ILIKE says use the wild card and ignore case SELECT name FROM us_states WHERE name ILIKE ‘new%’ name New Hampshire New Jersey etc Nice!

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You can check if a value is in a set of options SELECT cartodb_id FROM us_states WHERE cartodb_id IN (1,4,99) name 1 4 Not 99 states

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Always watch your quotes! ‘text’

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Constrain results with ORDER BY & LIMIT

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The LIMIT restricts the number or rows you ask for SELECT cartodb_id FROM us_states WHERE cartodb_id > 24 LIMIT 1 cartodb_id 25 see!

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The LIMIT restricts the number or rows you ask for SELECT cartodb_id FROM us_states WHERE cartodb_id > 24 LIMIT 2 cartodb_id 25 26 see!

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The ORDER BY allows you to change the order of rows in your result SELECT cartodb_id FROM us_states WHERE cartodb_id > 24 ORDER BY cartodb_id cartodb_id 25 26 etc The default is determined by the order on disk. no noticeable change here

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ASC means, ascending. SELECT cartodb_id FROM us_states WHERE cartodb_id > 24 ORDER BY cartodb_id ASC cartodb_id 25 26 etc Here, ascending is the same as what we saw before

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DESC means, descending SELECT cartodb_id FROM us_states WHERE cartodb_id > 24 ORDER BY cartodb_id DESC cartodb_id 52 51 etc See!

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It works just as well on the alphabet SELECT name FROM us_states ORDER BY name DESC name Wyoming Wisconsin etc See!

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USING GROUP BY

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Let’s do something crazy here, substring() SELECT substring(name,1,3) FROM us_states substring Ala Ala Ari etc So we are asking for the ﬁrst 3 characters of the state name

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We can add a GROUP BY, which will join all rows with the same results SELECT substring(name,1,2) FROM us_states GROUP BY substring(name,1,2) substring Ala Ari Ark etc The second ‘Ala’ disappeared from our results

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We can now use an aggregate that will work within each group result, not across the who table SELECT substring(name,1,2), count(*) FROM us_states GROUP BY substring(name,1,2) substring count Ala 2 Ari 1 Ark 1 etc Now we can see where that second ‘Ala’ went to!

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Let’s find out what the most common first three letters of state names is by including an ORDER BY SELECT substring(name,1,2), count(*) FROM us_states GROUP BY substring(name,1,2) ORDER BY count(*) DESC substring count New 4 Nor 2 Ala 2 etc So ‘New’ has 4, which we all experience from trying to quickly fill online address forms…

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SQL Joins to query across tables

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Someone else has done a really nice job of describing and illustrating this, so I’m going to shamelessly reuse

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http://bit.ly/1vjVAUA

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http://bit.ly/1vjVAUA

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http://bit.ly/1vjVAUA

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http://bit.ly/1vjVAUA

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http://bit.ly/1vjVAUA

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http://bit.ly/1vjVAUA

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http://bit.ly/1vjVAUA

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http://bit.ly/1vjVAUA

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I also love nested queries

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Here, I use a nested queries and compare against the results for a state name SELECT name, state FROM us_cities WHERE state IN (SELECT name FROM us_states WHERE name ILIKE ‘new y%’) name state New York New York Albany New York Buffalo New York etc

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You can also nest right in the column results! SELECT name, (SELECT population FROM us_census WHERE name = u.name) pop FROM us_cities u ORDER BY pop DESC name pop California 38000000 Texas 26000000 New York 20000000 etc

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The CTE or the “with” statement

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You can use a WITH statement to treat a SQL result as a new table WITH fakestuff AS (SELECT name, state FROM us_cities WHERE name = ‘New York’) SELECT * FROM fakestuff name state New York New York Here, I create a magical new table- like object I call “fakestuﬀ” When I select ‘*’ from the fakestuﬀ table- ish thing, the result is only one row

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I use them to help organize my thinking in SQL WITH fakestuff AS (SELECT ST_Centroid(the_geom) AS the_geom, name, pop FROM us_states) SELECT * FROM fakestuff WHERE pop > 5000000 name state New York New York

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DELETES & UPDATES

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UPDATES work with all the same properties of SELECT but with a few diﬀerences. UPDATE us_states SET name = ‘New Amsterdam’ WHERE name = ‘New York’ For example, you will only get a conﬁrmation result, not actual rows. (typically)

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Same goes for DELETE statements DELETE FROM us_states WHERE name = ‘New York’

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An very basic introduction to spatial SQL

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SELECT the_geom FROM us_states In CartoDB, your geometries are always standardized and stored in a column called the_geom the_geom MULTIPOLYGON… MULTIPOLYGON… etc

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SELECT the_geom FROM us_states ORDER BY the_geom ASC You can manipulate, ﬁlter, order, measure and lots of other things to geometries the_geom MULTIPOLYGON… MULTIPOLYGON… etc

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SELECT the_geom FROM us_states WHERE ST_Intersects(the_geom, my_geometry) CartoDB uses PostGIS to enable most geospatial functions. These are the functions that start with ‘ST_…’ the_geom MULTIPOLYGON…

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SELECT the_geom FROM us_states WHERE ST_Intersects(the_geom, CDB_(42, -80)) We also have a couple of helper functions only in CartoDB, these start with CDB_. For example, CDB_LatLng(num, num) creates a geometry from two coordinates the_geom MULTIPOLYGON…

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SELECT the_geom FROM us_states WHERE ST_Intersects(the_geom, CDB_(42, -80)) ST_Intersects returns TRUE if the two geometries intersect one another

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SELECT the_geom FROM us_states WHERE ST_DWithin(the_geom, CDB_(42, -80), 1) ST_DWithin returns TRUE if the two geometries are within a minimum distance (supplied as the 3rd variable)

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SELECT the_geom FROM us_states WHERE ST_DWithin(the_geom, CDB_(42, -80), 1) In CartoDB, we rely on projections. Using functions where we supply a unit measurement, that unit is always in the unit of the projection. Here, 1 degree lat/lng. Somewhat meaningless.

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SELECT the_geom FROM us_states WHERE ST_DWithin(the_geom::geography, CDB_LatLng(42, -80)::geography, 10000) You can use a spherical globe and measure in meters by re-casting the geometries to geography types (on the ﬂy)

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SELECT the_geom FROM us_states ORDER BY the_geom <-> CDB_LatLng(44,-80) You can ORDER BY the distance to a speciﬁed geometry really nicely in CartoDB using <->

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SELECT *, ST_Distance(the_geom, CDB_LatLng(42,-80)) AS d FROM us_states You can also use spatial measurements to derive a new or modiﬁed column result on the ﬂy

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SELECT *, ST_Distance(the_geom, CDB_LatLng(42,-80)) AS d FROM us_states ST_Distance will give me back the distance between two geometries. Here, the geometry in every row of my table would be measured from the point 42, -80

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SELECT *, ST_Distance(the_geom::geography, CDB_LatLng(42,-80)::geography) AS d FROM us_states Remember, this returns a unit, so we might want to ensure we get meters

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SELECT name, ST_MakeLine(the_geom, CDB_LatLng(42,-80)) AS the_geom FROM us_cities You can use ST_MakeLine to create lines between points on the ﬂy. Here, I’ll turn every city point, into a line between that point and my point at 42, -80

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UPDATE us_cities SET the_geom = ST_MakeLine(the_geom, CDB_LatLng(42,-80)) AS the_geom You can use all the stuﬀ in UPDATES to store results as well. If I ran this, my cities would no longer be points, but would be lines! Careful, there is no UNDO!

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SELECT name, ST_Centroid(the_geom) AS the_geom FROM us_states Likewise, we can turn polygons or lines into points. There are multiple ways to do this. ST_Centroid is one

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SELECT name, ST_Envelope(the_geom) AS the_geom FROM us_cities GROUP BY state Or you could turn points into a polygon. ST_Envelope will create the minimum box around a group of points

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the_geom versus the_geom_webmercator

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the_geom the_geom_webmercator the_geom_webmercator When you create new data, it goes into, Behind the scenes, CartoDB also translates it to, When CartoDB draws a map, it comes from,

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the_geom the_geom_webmercator Measure using Latitude, Longitude with,

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the_geom the_geom_webmercator Select data to “Create new table” and include,

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the_geom the_geom_webmercator Select or ﬁlter data you want on the map, include,

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the_geom the_geom_webmercator CartoDB can’t render the map without

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the_geom the_geom_webmercator Anytime you modify and store data on CartoDB will update so your maps are accurate,

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the_geom the_geom_webmercator If you modify on the ﬂy and want to map results, you need to create on the ﬂy also!

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SELECT cartodb_id, the_geom, the_geom_webmercator FROM us_states ORDER BY the_geom <-> CDB_LatLng(44,-80) You can do any ﬁlter you want, just make sure you include the_geom_webmercator in your column results to show it on the map

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SELECT cartodb_id, ST_Buffer(the_geom, 1) FROM us_cities This wont display anything on your map, because it lacks the_geom_webmercator

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SELECT cartodb_id, ST_Buffer(the_geom, 1), the_geom_webmercator FROM us_cities This would show the original points, but it would fail to include the transformed (buﬀered) point because the_geom hasn’t been altered on disk

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SELECT cartodb_id, ST_Buffer(the_geom, 1) AS the_geom_webmercator FROM us_cities Just aliasing to the_geom_webmercator wont work, remember the_geom is a diﬀerent projection than the_geom_webmercator

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SELECT cartodb_id, ST_Transform(ST_Buffer(the_geom, 1), 3857) AS the_geom_webmercator FROM us_cities ST_Transform to the rescue!

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SELECT cartodb_id, ST_Transform(ST_Buffer(the_geom, 1), 3857) AS the_geom_webmercator FROM us_cities ST_Transform turns projects any geometry from one projection to another. We want to go from WGS84 (the_geom) to Web Mercator (the_geom_webmercator)

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SELECT cartodb_id, ST_Transform(ST_Buffer(the_geom, 1), 3857) AS the_geom_webmercator FROM us_cities In CartoDB, webmercator has an ID, or a SRID to be exact, that we can use to tell ST_Transform that we want webmercator from the_geom. The SRID is 3857 3857 = Web Mercator

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SELECT cartodb_id, ST_Transform(ST_Buffer(the_geom, 1), 3857) AS the_geom_webmercator FROM us_cities Now our result will be on the map dynamically!

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You can update the SQL of any layer in your visualizations with the setSQL function ! layer.setSQL(“SELECT * FROM us_states WHERE name = ‘New Hampshire’”); In CartoDB.js http://bit.ly/1t8vK1i

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http://bit.ly/short-sql Don’t forget, more SQL examples

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Let’s look at some of this live!

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Maps, Lies & Storytelling Part 7 - More on CartoCSS

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Once you’ve got the things you want to render, you will deﬁne a set of rules in CartoCSS to deﬁne how you render it

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CartoCSS in CartoDB Can be viewed as the entry point for cartography and design

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a few of the basics

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#table{ polygon-fill: #FF6600; polygon-opacity: 0.7; line-color: #FFF; line-width: 1; line-opacity: 1; }

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#table{ polygon-fill: red; polygon-opacity: 0.7; line-color: #FFF; line-width: 1; line-opacity: 1; }

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#table{ polygon-fill: rgb(0,0,256); polygon-opacity: 0.7; line-color: #FFF; line-width: 1; line-opacity: 1; }

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#table{ polygon-fill: #AEFF39; polygon-opacity: 0.2; line-color: #FFF; line-width: 1; line-opacity: 1; }

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#table{ polygon-fill: #E94C6F; polygon-opacity: 0.9; line-color: #542733; line-width: 4; line-opacity: 1; }

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#table{ marker-fill-opacity: 0.9; marker-line-color: #FFF; marker-line-width: 1.5; marker-line-opacity: 1; marker-placement: point; marker-type: ellipse; marker-width: 10; marker-fill: #FF6600; marker-allow-overlap: true; }

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#table{ marker-fill-opacity: 0.9; marker-line-color: #FFF; marker-line-width: 1.5; marker-line-opacity: 1; marker-placement: point; marker-type: ellipse; marker-width: 22; marker-fill: #FF6600; marker-allow-overlap: true; }

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#table{ marker-fill-opacity: 0.9; marker-line-color: yellow; marker-line-width: 3.5; marker-line-opacity: 1; marker-placement: point; marker-type: ellipse; marker-width: 22; marker-fill: #FF6600; marker-allow-overlap: true; }

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lines use the same rules as the line borders of polygons

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#table{ line-color: #FF6600; line-width: 2; line-opacity: 0.7; }

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#table{ line-color: #FF6600; line-width: 15; line-opacity: 0.7; }

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#table{ line-color: #FF6600; line-width: 15; line-opacity: 0.7; line-smooth: .5; }

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#table{ line-color: #FF6600; line-width: 15; line-opacity: 0.7; line-smooth: 5.5; }

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#table{ line-color: #FF6600; line-width: 15; line-opacity: 0.7; line-smooth: 5.5; line-dasharray: 5, 4; }

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all of these are subject to composite operations. we’ll see in the hands on

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Using rules

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Any column in your table (or SQL result) can be used to deﬁne a rule

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#table{ marker-fill-opacity: 0.9; marker-line-color: yellow; marker-line-width: 1.5; marker-line-opacity: 1; marker-width: 22; marker-fill: #FF6600; [atype = 'Specialized']{ marker-fill: white; } } ‘atype’ is a string column in my table

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#table{ marker-fill-opacity: 0.9; marker-line-color: blue; marker-line-width: 1.5; marker-line-opacity: 1; marker-width: 22; marker-fill: #FF6600; [atype = 'Institutional']{ marker-fill: green; [cartodb_id<12594]{ marker-line-color: red; } } } nested

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#table{ marker-fill-opacity: 0.9; marker-line-color: blue; marker-line-width: 1.5; marker-line-opacity: 1; marker-width: 22; marker-fill: #FF6600; [is_valid = True]{ marker-fill: green; } } Boolean columns a slightly diﬀerent in notation

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Zoom as a parameter

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#table{ marker-fill-opacity: 0.9; marker-line-color: blue; marker-line-width: 1.5; marker-line-opacity: 1; marker-width: 22; marker-fill: #FF6600; [zoom > 7]{ marker-line-width: 3; } } The zoom parameter

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A whole world of labels and text…

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http://bit.ly/1rtx6pt

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Client side CartoCSS | Torque

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Client side CartoCSS | Torque

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Map { -torque-frame-count:512; -torque-animation-duration:30; -torque-time- attribute:"cartodb_id"; -torque-aggregation- function:"count(cartodb_id)"; -torque-resolution:2; -torque-data- aggregation:linear; } The ‘Map’ object sets up the entire canvas and settings of your temporal visualization

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Map { -torque-frame-count:512; -torque-animation-duration:30; -torque-time- attribute:"cartodb_id"; -torque-aggregation- function:"count(cartodb_id)"; -torque-resolution:2; -torque-data- aggregation:linear; } frame-count is how many temporal bins your data will be broken into. if you are visualizing 31 days, you wouldn’t need more than 31…

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Map { -torque-frame-count:512; -torque-animation-duration:30; -torque-time- attribute:"cartodb_id"; -torque-aggregation- function:"count(cartodb_id)"; -torque-resolution:2; -torque-data- aggregation:linear; } duration is how long the animation will play for before looping back to the beginning

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Map { -torque-frame-count:512; -torque-animation-duration:30; -torque-time- attribute:"cartodb_id"; -torque-aggregation- function:"count(cartodb_id)"; -torque-resolution:2; -torque-data- aggregation:linear; } time-attribute is the numerical or temporal column in your dataset that you want to order the animation by, beginning to end

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Map { -torque-frame-count:512; -torque-animation-duration:30; -torque-time- attribute:"cartodb_id"; -torque-aggregation- function:"count(cartodb_id)"; -torque-resolution:2; -torque-data- aggregation:linear; } the aggregation- function is actually SQL! It is how you want data in the same spatial cell, in the same temporal bin, to be combined. here, you just get a raw value

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Map { -torque-frame-count:512; -torque-animation-duration:30; -torque-time- attribute:"cartodb_id"; -torque-aggregation- function:"sum(population)"; -torque-resolution:2; -torque-data- aggregation:linear; } But here, you would get a totally diﬀerent value. The result of this is an attribute called value that we can use to style with in our CartoCSS later

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#table{ comp-op: lighter; marker-fill-opacity: 0.9; marker-line-color: #FFF; marker-line-width: 1.5; marker-line-opacity: 1; marker-type: ellipse; marker-width: 6; marker-fill: #FF9900; } Much of the CartoCSS for the rest looks the same as we saw before.

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#table{ comp-op: lighter; marker-fill-opacity: 0.9; marker-line-color: #FFF; marker-line-width: 1.5; marker-line-opacity: 1; marker-type: ellipse; marker-width: 6; marker-fill: #FF9900; [value > 3]{ marker-fill-color: blue; } } We can use the value attribute from our aggregation function here to change styles though

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#table[frame-offset=1] { marker-width:8; marker-fill-opacity:0.45; } We can also use frame-oﬀsets ! frame-oﬀsets allow you to draw the marker a second (or more times) after the frame it initially hits your maps.

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You can update the CartoCSS of any layer in your visualizations with the setCartoCSS function (no line breaks) ! layer.setCartoCSS(“#tablename {marker- fill: red; }”); In CartoDB.js http://bit.ly/1vpF1pi

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Maps, Lies & Storytelling Technology aside - Odyssey.js

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hands on

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Maps, Lies & Storytelling Guest speaker

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Aurelia Moser Knight News Fellow Ushahidi / Internews http://aureliamoser.com/ Next week

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Maps, Lies & Storytelling Discussion

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Maps, Lies & Storytelling Assignments

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Readings 5 Favorite Maps: Bill Rankin | http://bit.ly/1nrXEsV 15 Maps that explain ISIS | http://bit.ly/1pl02wx

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I want you to show me the most far out way you can use CartoCSS and/or SQL to create a visualization. It can be a multistep process (UPDATEs, Table from Query), but be sure to document and share every step of your process. Assignment 1

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Example 1

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Example 2

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Time to document your lie. Begin creating a comprehensive map and story of your lie. This should be a narrative story. During the narrative, you should highlight where your lie is exposed through maps. Assignment 2

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Create a map to accompany a current news article or a current event. This should be an original map, not some improvement of a map included in the article Assignment 3

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See you next week!