Update release dir with make release

preemptively set_srid for obs_getavailableboundaries

NEWS.md

@@ -1,3 +1,62 @@
-0.0.1 (open date)
+1.0.1 (2016-07-01)
+---
+
+__Bugfixes__
+
+* Fix for ERROR:  Operation on mixed SRID geometries #130
+
+
+1.0.0 (6/27/2016)
+-----
+
+* Incremented to 1.0.0 to be in compliance with [SemVer](http://semver.org/),
+  which disallows use of 0.x.x versions.  This also reflects that we are
+  already in production.
+
+__API Changes__
+
+* Added `OBS_DumpVersion` to look up version data ([#118](https://github.com/CartoDB/observatory-extension/pull/118))
+
+__Improvements__
+
+* Whether data exists for a geom now determined by polygon intersection instead of
+  BBOX overlap ([#119](https://github.com/CartoDB/observatory-extension/pull/119))
+* Automated tests cover Spanish and UK data
+  ([#115](https://github.com/CartoDB/observatory-extension/pull/115))
+* Automated tests cover `OBS_GetUSCensusMeasure`
+  ([#105](https://github.com/CartoDB/observatory-extension/pull/105))
+
+__Bugfixes__
+
+* Geom table can have different `geomref_colname` than the data table
+  ([#123](https://github.com/CartoDB/observatory-extension/pull/123))
+
+
+0.0.5 (5/27/2016)
+-----
+* Adds new function `OBS_GetMeasureById` ([#96](https://github.com/CartoDB/observatory-extension/pull/96))
+
+0.0.4 (5/25/2016)
+-----
+* Updates queries involving US Census measure tags to be more generic ([#95](https://github.com/CartoDB/observatory-extension/pull/95))
+* Fixes tests which relied on an erroneous subset of block groups ([#95](https://github.com/CartoDB/observatory-extension/pull/95))
+
+0.0.3 (5/24/2016)
+-----
+* Generalizes internal queries to properly pull from multiple named geometry references
+* Adds tests for Who's on First boundaries
+* Improves automatic fixtures testing script
+
+0.0.2 (5/19/2016)
+-----
+* Adds Data Observatory exploration functions
+* Adds Data Observatory boundary functions
+* Adds Data Observatory measure functions
+* Adds script to generate fixtures for tests
+* Adds script for the automatic testing of metadata
+* Adds full documentation for all included functions
+* removes `cartodb` extension dependency
+
+0.0.1 (5/19/2016)
 ------------------
-* First iteration of `OBS_GetDemographicSnapshot(location Geometry(Point,4326))`;
+* First iteration of `OBS_GetDemographicSnapshot(location Geometry(Point,4326))`

RELEASE.md

@@ -20,12 +20,6 @@ script for the new release, `release/observatory--X.Y.Z.sql`:
 make release
 ```
 
-Then, the release manager shall produce upgrade and downgrade scripts
-to migrate to/from the previous release. In the case of minor/patch
-releases this simply consist in extracting the functions that have changed
-and placing them in the proper `release/observatory--X.Y.Z--A.B.C.sql`
-file.
-
 The new release can be deployed for staging/smoke tests with this command:
 
 ```

doc/API.md

@@ -4,6 +4,8 @@ This file is for reference purposes only. It is intended for tracking the Data O
 
 ## Documentation
 
+* Overview (local file in the Docs repo)
+* Accessing the Data Observatory (local file in the Docs repo)
 * [Measures Functions](measures_functions.md)
 * [Boundary Functions](boundary_functions.md)
 * [Discovery Functions](discovery_functions.md)

doc/glossary.md

@@ -29,7 +29,7 @@ Disputed Areas  |  whosonfirst.wof_disputed_geom  |  none
 
 ## OBS_GetUSCensusMeasure Names Table
 
-This list contains human readable names accepted in the ```OBS_GetUSCensusMeasure``` function. For the more comprehensive list of columns available to the ```OBS_GetMeasure``` function, see the [Data Observatory Catalog](https://cartodb.github.io/bigmetadata/observatory.pdf).
+This list contains human readable names accepted in the ```OBS_GetUSCensusMeasure``` function. For the more comprehensive list of columns available to the ```OBS_GetMeasure``` function, see the [Data Observatory Catalog](http://data-observatory.s3.amazonaws.com/observatory.pdf).
 
 Measure name             |  Measure description
 ------------------------ | --------------------

doc/license.md

@@ -1,6 +1,8 @@
 # License
 
-The Data Observatory is a collection of various sources of data with varying licenses. We have worked hard to find you data that will work for the broadest set of use-cases. For competency, please still review the terms for any dataset you use and respect the rights of the owners for each dataset. The following third-party data sources are used in the Data Observatory, and we have included the links to the terms governing their use.
+The Data Observatory is a collection of data sources with varying licenses and terms of use. We have endeavored to find you data that will work for the broadest set of use-cases. The following third-party data sources are used in the Data Observatory, and we have included the links to the terms governing their use.
+
+_**Legal Note**: The Data Observatory makes use of a variety of third party data and databases (collectively, the “Data”). You acknowledge that the included Data, and the licenses and terms of use, may be amended from time to time. Whenever you use the Data, you agree to the current relevant terms or license. Some Data will require that you provide attribution to the data source. Other Data may be protected by US or international copyright laws, treaties, or conventions. The Data and associated metadata are provided 'as-is', without express or implied warranty of any kind, including, but not limited to, infringement, merchantability and fitness for a particular purpose. CartoDB is not responsible for the accuracy, completeness, timeliness or quality of the Data._
 
 Name  | Terms link 
 -------|---------
@@ -14,5 +16,5 @@ Natural Earth  |  [http://www.naturalearthdata.com/about/terms-of-use/](http://w
 Quattroshapes  |  [https://github.com/foursquare/quattroshapes/blob/master/LICENSE.md](https://github.com/foursquare/quattroshapes/blob/master/LICENSE.md)
 Zetashapes  |  [http://zetashapes.com/license](http://zetashapes.com/license)
 Spielman & Singleton  |  [https://www.openicpsr.org/repoEntity/show/41329](https://www.openicpsr.org/repoEntity/show/41329)
-Instituto Nacional de Estadistica  |  [http://www.ine.es/ss/Satellite?L=0&c=Page&cid=1254735849170&p=1254735849170&pagename=Ayuda%2FINELayout](http://www.ine.es/ss/Satellite?L=0&c=Page&cid=1254735849170&p=1254735849170&pagename=Ayuda%2FINELayout)
+El Instituto Nacional de Estadística (INE) | The National Statistics Institute (INE) of Spain includes data from multiple sources. If you are re-using their data, they explicitly require that you reference them accordingly<br /><br />[http://www.ine.es/ss/Satellite?L=0&c=Page&cid=1254735849170&p=1254735849170&pagename=Ayuda%2FINELayout](http://www.ine.es/ss/Satellite?L=0&c=Page&cid=1254735849170&p=1254735849170&pagename=Ayuda%2FINELayout)
 

doc/measures_functions.md

@@ -2,7 +2,7 @@
 
 [Data Observatory Measures](/cartodb-platform/data/overview/#measures-methods) are the numerical location data you can access. The measure functions allow you to access individual measures to augment your own data or integrate in your analysis workflows. Measures are used by sending an identifier or a geometry (point or polygon) and receiving back a measure (an absolute value) for that location.
 
-There are hundreds of measures and the list is growing with each release. You can currently discover and learn about measures contained in the Data Observatory by downloading our [Data Catalog](https://cartodb.github.io/bigmetadata/observatory.pdf).
+There are hundreds of measures and the list is growing with each release. You can currently discover and learn about measures contained in the Data Observatory by downloading our [Data Catalog](http://data-observatory.s3.amazonaws.com/observatory.pdf).
 
 You can [access](/cartodb-platform/data/accessing/#accessing-the-data-observatory) measures through the CartoDB Editor. The same methods will work if you are using the CartoDB Platform to develop your application. We [encourage you](/cartodb-platform/data/accessing/#best-practices) to use table modifying methods (UPDATE and INSERT) over dynamic methods (SELECT).
 
@@ -16,7 +16,7 @@ Name |Description
 --- | ---
 point | a WGS84 point geometry (the_geom)
 measure_name | a human readable name of a US Census variable. The list of measure_names is [available in the glossary](/cartodb-platform/data/glossary/#obsgetuscensusmeasure-names-table).
-normalize | for measures that are are **sums** (e.g. population) the default normalization is 'area' and response comes back as a rate per square kilometer. Other options are 'denominator', which will use the denominator specified in the [Data Catalog](http://cartodb.github.io/bigmetadata/index.html) (optional)
+normalize | for measures that are are **sums** (e.g. population) the default normalization is 'area' and response comes back as a rate per square kilometer. Other options are 'denominator', which will use the denominator specified in the [Data Catalog](http://data-observatory.s3.amazonaws.com/observatory.pdf) (optional)
 boundary_id | source of geometries to pull measure from (e.g., 'us.census.tiger.census_tract')
 time_span | time span of interest (e.g., 2010 - 2014)
 
@@ -47,7 +47,7 @@ Name |Description
 --- | ---
 polygon | a WGS84 polygon geometry (the_geom)
 measure_name | a human readable string name of a US Census variable. The list of measure_names is [available in the glossary](/cartodb-platform/data/glossary/#obsgetuscensusmeasure-names-table).
-normalize | for measures that are **sums** (e.g. population) the default normalization is 'none' and response comes back as a raw value. Other options are 'denominator', which will use the denominator specified in the [Data Catalog](https://cartodb.github.io/bigmetadata/observatory.pdf) (optional)
+normalize | for measures that are **sums** (e.g. population) the default normalization is 'none' and response comes back as a raw value. Other options are 'denominator', which will use the denominator specified in the [Data Catalog](http://data-observatory.s3.amazonaws.com/observatory.pdf) (optional)
 boundary_id | source of geometries to pull measure from (e.g., 'us.census.tiger.census_tract')
 time_span | time span of interest (e.g., 2010 - 2014)
 
@@ -70,7 +70,7 @@ SET local_male_population = OBS_GetUSCensusMeasure(the_geom, 'Male Population')
 
 ## OBS_GetMeasure(point geometry, measure_id text)
 
-The ```OBS_GetMeasure(point, measure_id)``` function returns any Data Observatory measure at a point location. You can browse all available Measures in the [Catalog](https://cartodb.github.io/bigmetadata/observatory.pdf)).
+The ```OBS_GetMeasure(point, measure_id)``` function returns any Data Observatory measure at a point location. You can browse all available Measures in the [Catalog](http://data-observatory.s3.amazonaws.com/observatory.pdf).
 
 #### Arguments
 
@@ -78,7 +78,7 @@ Name |Description
 --- | ---
 point | a WGS84 point geometry (the_geom)
 measure_id | a measure identifier from the Data Observatory ([see available measures](https://cartodb.github.io/bigmetadata/observatory.pdf)). It is important to note that these are different than 'measure_name' used in the Census based functions above.
-normalize | for measures that are are **sums** (e.g. population) the default normalization is 'area' and response comes back as a rate per square kilometer. The other option is 'denominator', which will use the denominator specified in the [Data Catalog](https://cartodb.github.io/bigmetadata/observatory.pdf). (optional)
+normalize | for measures that are are **sums** (e.g. population) the default normalization is 'area' and response comes back as a rate per square kilometer. The other option is 'denominator', which will use the denominator specified in the [Data Catalog](http://data-observatory.s3.amazonaws.com/observatory.pdf). (optional)
 boundary_id | source of geometries to pull measure from (e.g., 'us.census.tiger.census_tract')
 time_span | time span of interest (e.g., 2010 - 2014)
 
@@ -109,7 +109,7 @@ Name |Description
 --- | ---
 polygon_geometry | a WGS84 polygon geometry (the_geom)
 measure_id | a measure identifier from the Data Observatory ([see available measures](https://cartodb.github.io/bigmetadata/observatory.pdf))  
-normalize | for measures that are are **sums** (e.g. population) the default normalization is 'none' and response comes back as a raw value. Other options are 'denominator', which will use the denominator specified in the [Data Catalog](https://cartodb.github.io/bigmetadata/observatory.pdf) (optional)
+normalize | for measures that are are **sums** (e.g. population) the default normalization is 'none' and response comes back as a raw value. Other options are 'denominator', which will use the denominator specified in the [Data Catalog](http://data-observatory.s3.amazonaws.com/observatory.pdf) (optional)
 boundary_id | source of geometries to pull measure from (e.g., 'us.census.tiger.census_tract')
 time_span | time span of interest (e.g., 2010 - 2014)
 
@@ -134,9 +134,43 @@ SET household_count = OBS_GetMeasure(the_geom, 'us.census.acs.B11001001')
 
 * If an unrecognized normalization type is input, raise an error: `'Only valid inputs for "normalize" are "area" (default) and "denominator".`
 
+## OBS_GetMeasureById(geom_ref text, measure_id text, boundary_id text)
+
+The ```OBS_GetMeasureById(geom_ref, measure_id, boundary_id)``` function returns any Data Observatory measure that corresponds to the boundary in ```boundary_id``` that has a geometry reference of ```geom_ref```.
+
+#### Arguments
+
+Name |Description
+--- | ---
+geom_ref | a geometry reference (e.g., a US Census geoid)
+measure_id | a measure identifier from the Data Observatory ([see available measures](https://cartodb.github.io/bigmetadata/observatory.pdf))  
+boundary_id | source of geometries to pull measure from (e.g., 'us.census.tiger.census_tract')
+time_span (optional) | time span of interest (e.g., 2010 - 2014). If `NULL` is passed, the measure from the most recent data will be used.
+
+#### Returns
+
+A NUMERIC value
+
+Key | Description
+--- | ---
+value | the raw measure associated with `geom_ref`
+
+#### Example
+
+Add a measure to an empty column based on county geoids in your table
+
+```SQL
+UPDATE tablename
+SET household_count = OBS_GetMeasureById(geoid_column, 'us.census.acs.B11001001', 'us.census.tiger.county')
+```
+
+#### Errors
+
+* Returns `NULL` if there is a mismatch between the geometry reference and the boundary id such as using the geoid of a county with the boundary of block groups
+
 ## OBS_GetCategory(point geometry, category_id text)
 
-The ```OBS_GetCategory(point, category_id)``` function returns any Data Observatory Category value at a point location. The Categories available are currently limited to Segmentation categories. See the Segmentation section of the [Catalog](https://cartodb.github.io/bigmetadata/observatory.pdf) for more detail.
+The ```OBS_GetCategory(point, category_id)``` function returns any Data Observatory Category value at a point location. The Categories available are currently limited to Segmentation categories. See the Segmentation section of the [Catalog](http://data-observatory.s3.amazonaws.com/observatory.pdf) for more detail.
 
 #### Arguments
 

release/observatory.control

@@ -1,5 +1,5 @@
 comment = 'CartoDB Observatory backend extension'
-default_version = '0.0.3'
+default_version = '1.0.1'
 requires = 'postgis'
 superuser = true
 schema = cdb_observatory

scripts/autotest.py

@@ -24,8 +24,9 @@ def query(q, is_meta=False, **options):
     params['api_key'] = META_API_KEY if is_meta else API_KEY
     return requests.get(url, params=params)
 
-MEASURE_COLUMNS = [(r['id'], ) for r in query('''
-SELECT id FROM obs_column
+MEASURE_COLUMNS = [(r['id'], r['point_only'], ) for r in query('''
+SELECT id, aggregate NOT ILIKE 'sum' as point_only
+FROM obs_column
 WHERE type ILIKE 'numeric'
 AND weight > 0
 ''', is_meta=True).json()['rows']]
@@ -42,7 +43,16 @@ WHERE type ILIKE 'geometry'
 AND weight > 0
 ''', is_meta=True).json()['rows']]
 
-def default_point(column_id):
+US_CENSUS_MEASURE_COLUMNS = [(r['name'], ) for r in query('''
+SELECT c.name FROM obs_column c, obs_column_tag ct
+WHERE type ILIKE 'numeric'
+AND c.id = ct.column_id
+AND ct.tag_id LIKE 'us.census%'
+AND weight > 0
+''', is_meta=True).json()['rows']]
+
+
+def default_geometry_id(column_id):
     '''
     Returns default test point for the column_id.
     '''
@@ -63,8 +73,71 @@ def default_point(column_id):
         return 'CDB_LatLng(40.7, -73.9)'
 
 
+def default_point(column_id):
+    '''
+    Returns default test point for the column_id.
+    '''
+    if column_id == 'whosonfirst.wof_disputed_geom':
+        return 'CDB_LatLng(33.78, 76.57)'
+    elif column_id == 'whosonfirst.wof_marinearea_geom':
+        return 'CDB_LatLng(43.33, -68.47)'
+    elif column_id in ('us.census.tiger.school_district_elementary',
+                       'us.census.tiger.school_district_secondary',
+                       'us.census.tiger.school_district_elementary_clipped',
+                       'us.census.tiger.school_district_secondary_clipped'):
+        return 'CDB_LatLng(40.7025, -73.7067)'
+    elif column_id.startswith('uk'):
+        if 'WA' in column_id:
+            return 'CDB_LatLng(51.46844551219723, -3.184833526611328)'
+        else:
+            return 'CDB_LatLng(51.51461834694225, -0.08883476257324219)'
+    elif column_id.startswith('es'):
+        return 'CDB_LatLng(42.8226119029222, -2.51141249535454)'
+    elif column_id.startswith('us.zillow'):
+        return 'CDB_LatLng(28.3305906291771, -81.3544048197256)'
+    else:
+        return 'CDB_LatLng(40.7, -73.9)'
+
+
+def default_area(column_id):
+    '''
+    Returns default test area for the column_id
+    '''
+    point = default_point(column_id)
+    area = 'ST_Transform(ST_Buffer(ST_Transform({point}, 3857), 1000), 4326)'.format(
+        point=point)
+    return area
+
+@parameterized(US_CENSUS_MEASURE_COLUMNS)
+def test_get_us_census_measure_points(name):
+    resp = query('''
+SELECT * FROM {schema}OBS_GetUSCensusMeasure({point}, '{name}')
+                 '''.format(name=name.replace("'", "''"),
+                            schema='cdb_observatory.' if USE_SCHEMA else '',
+                            point=default_point('')))
+    assert_equal(resp.status_code, 200)
+    rows = resp.json()['rows']
+    assert_equal(1, len(rows))
+    assert_is_not_none(rows[0].values()[0])
+
+
 @parameterized(MEASURE_COLUMNS)
-def test_measure_points(column_id):
+def test_get_measure_areas(column_id, point_only):
+    if point_only:
+        return
+    resp = query('''
+SELECT * FROM {schema}OBS_GetMeasure({area}, '{column_id}')
+                 '''.format(column_id=column_id,
+                            schema='cdb_observatory.' if USE_SCHEMA else '',
+                            area=default_area(column_id)))
+    assert_equal(resp.status_code, 200)
+    rows = resp.json()['rows']
+    assert_equal(1, len(rows))
+    assert_is_not_none(rows[0].values()[0])
+
+
+@parameterized(MEASURE_COLUMNS)
+def test_get_measure_points(column_id, point_only):
     resp = query('''
 SELECT * FROM {schema}OBS_GetMeasure({point}, '{column_id}')
                  '''.format(column_id=column_id,
@@ -75,8 +148,20 @@ SELECT * FROM {schema}OBS_GetMeasure({point}, '{column_id}')
     assert_equal(1, len(rows))
     assert_is_not_none(rows[0].values()[0])
 
+#@parameterized(CATEGORY_COLUMNS)
+#def test_get_category_areas(column_id):
+#    resp = query('''
+#SELECT * FROM {schema}OBS_GetCategory({area}, '{column_id}')
+#                 '''.format(column_id=column_id,
+#                            schema='cdb_observatory.' if USE_SCHEMA else '',
+#                            area=default_area(column_id)))
+#    assert_equal(resp.status_code, 200)
+#    rows = resp.json()['rows']
+#    assert_equal(1, len(rows))
+#    assert_is_not_none(rows[0].values()[0])
+
 @parameterized(CATEGORY_COLUMNS)
-def test_category_points(column_id):
+def test_get_category_points(column_id):
     resp = query('''
 SELECT * FROM {schema}OBS_GetCategory({point}, '{column_id}')
                  '''.format(column_id=column_id,
@@ -87,14 +172,62 @@ SELECT * FROM {schema}OBS_GetCategory({point}, '{column_id}')
     assert_equal(1, len(rows))
     assert_is_not_none(rows[0].values()[0])
 
-@parameterized(BOUNDARY_COLUMNS)
-def test_boundary_points(column_id):
-    resp = query('''
-SELECT * FROM {schema}OBS_GetBoundary({point}, '{column_id}')
-                 '''.format(column_id=column_id,
-                            schema='cdb_observatory.' if USE_SCHEMA else '',
-                            point=default_point(column_id)))
-    assert_equal(resp.status_code, 200)
-    rows = resp.json()['rows']
-    assert_equal(1, len(rows))
-    assert_is_not_none(rows[0].values()[0])
+#@parameterized(BOUNDARY_COLUMNS)
+#def test_get_boundaries_by_geometry(column_id):
+#    resp = query('''
+#SELECT * FROM {schema}OBS_GetBoundariesByGeometry({area}, '{column_id}')
+#                 '''.format(column_id=column_id,
+#                            schema='cdb_observatory.' if USE_SCHEMA else '',
+#                            area=default_area(column_id)))
+#    assert_equal(resp.status_code, 200)
+#    rows = resp.json()['rows']
+#    assert_equal(1, len(rows))
+#    assert_is_not_none(rows[0].values()[0])
+
+#@parameterized(BOUNDARY_COLUMNS)
+#def test_get_points_by_geometry(column_id):
+#    resp = query('''
+#SELECT * FROM {schema}OBS_GetPointsByGeometry({area}, '{column_id}')
+#                 '''.format(column_id=column_id,
+#                            schema='cdb_observatory.' if USE_SCHEMA else '',
+#                            area=default_area(column_id)))
+#    assert_equal(resp.status_code, 200)
+#    rows = resp.json()['rows']
+#    assert_equal(1, len(rows))
+#    assert_is_not_none(rows[0].values()[0])
+
+#@parameterized(BOUNDARY_COLUMNS)
+#def test_get_boundary_points(column_id):
+#    resp = query('''
+#SELECT * FROM {schema}OBS_GetBoundary({point}, '{column_id}')
+#                 '''.format(column_id=column_id,
+#                            schema='cdb_observatory.' if USE_SCHEMA else '',
+#                            point=default_point(column_id)))
+#    assert_equal(resp.status_code, 200)
+#    rows = resp.json()['rows']
+#    assert_equal(1, len(rows))
+#    assert_is_not_none(rows[0].values()[0])
+
+#@parameterized(BOUNDARY_COLUMNS)
+#def test_get_boundary_id(column_id):
+#    resp = query('''
+#SELECT * FROM {schema}OBS_GetBoundaryId({point}, '{column_id}')
+#                 '''.format(column_id=column_id,
+#                            schema='cdb_observatory.' if USE_SCHEMA else '',
+#                            point=default_point(column_id)))
+#    assert_equal(resp.status_code, 200)
+#    rows = resp.json()['rows']
+#    assert_equal(1, len(rows))
+#    assert_is_not_none(rows[0].values()[0])
+
+#@parameterized(BOUNDARY_COLUMNS)
+#def test_get_boundary_by_id(column_id):
+#    resp = query('''
+#SELECT * FROM {schema}OBS_GetBoundaryById({geometry_id}, '{column_id}')
+#                 '''.format(column_id=column_id,
+#                            schema='cdb_observatory.' if USE_SCHEMA else '',
+#                            geometry_id=default_geometry_id(column_id)))
+#    assert_equal(resp.status_code, 200)
+#    rows = resp.json()['rows']
+#    assert_equal(1, len(rows))
+#    assert_is_not_none(rows[0].values()[0])

scripts/generate_fixtures.py

@@ -33,7 +33,8 @@ def select_star(tablename):
 
 cdb = Dumpr('observatory.cartodb.com','')
 
-metadata = ['obs_table', 'obs_column_table', 'obs_column', 'obs_column_tag', 'obs_tag', 'obs_column_to_column']
+metadata = ['obs_table', 'obs_column_table', 'obs_column', 'obs_column_tag',
+            'obs_tag', 'obs_column_to_column', 'obs_dump_version']
 
 fixtures = [
     ('us.census.tiger.census_tract', 'us.census.tiger.census_tract', '2014'),

src/pg/observatory.control

@@ -1,5 +1,5 @@
 comment = 'CartoDB Observatory backend extension'
-default_version = '0.0.3'
+default_version = '1.0.1'
 requires = 'postgis'
 superuser = true
 schema = cdb_observatory

src/pg/sql/40_observatory_utility.sql

@@ -199,3 +199,20 @@ BEGIN
   RETURN result;
 END;
 $$ LANGUAGE plpgsql;
+
+-- Function that returns the currently deployed obs_dump_version from the
+-- remote table of the same name.
+
+CREATE OR REPLACE FUNCTION cdb_observatory.OBS_DumpVersion(
+)
+  RETURNS TEXT
+AS $$
+DECLARE
+  result text;
+BEGIN
+  EXECUTE '
+    SELECT MAX(dump_id) FROM observatory.obs_dump_version
+  ' INTO result;
+  RETURN result;
+END;
+$$ LANGUAGE plpgsql;

src/pg/sql/41_observatory_augmentation.sql

@@ -380,6 +380,53 @@ END;
 $$ LANGUAGE plpgsql;
 
 
+CREATE OR REPLACE FUNCTION cdb_observatory.OBS_GetMeasureById(
+  geom_ref TEXT,
+  measure_id TEXT,
+  boundary_id TEXT,
+  time_span TEXT DEFAULT NULL
+)
+RETURNS NUMERIC
+AS $$
+DECLARE
+  target_table TEXT;
+  colname TEXT;
+  measure_val NUMERIC;
+  data_geoid_colname TEXT;
+  test_query TEXT;
+BEGIN
+
+  SELECT x ->> 'colname', x ->> 'tablename' INTO colname, target_table
+  FROM cdb_observatory._OBS_GetColumnData(boundary_id, Array[measure_id], time_span) As x;
+
+  EXECUTE
+    format('SELECT ct.colname
+              FROM observatory.obs_column_to_column c2c,
+                   observatory.obs_column_table ct,
+                   observatory.obs_table t
+             WHERE c2c.reltype = ''geom_ref''
+               AND ct.column_id = c2c.source_id
+               AND ct.table_id = t.id
+               AND t.tablename = %L'
+     , target_table)
+  INTO data_geoid_colname;
+
+  RAISE DEBUG 'target_table %, colname %', target_table, colname;
+
+  EXECUTE format(
+      'SELECT %I
+       FROM observatory.%I
+       WHERE %I.%I = %L',
+       colname,
+       target_table,
+       target_table, data_geoid_colname, geom_ref)
+  INTO measure_val;
+
+  RETURN measure_val;
+
+END;
+$$ LANGUAGE plpgsql;
+
 CREATE OR REPLACE FUNCTION cdb_observatory.OBS_GetCategory(
   geom geometry(Geometry, 4326),
   category_id TEXT,
@@ -435,7 +482,7 @@ BEGIN
        JOIN observatory.obs_column_tag ct
          ON c.id = ct.column_id
        WHERE cdb_observatory._OBS_StandardizeMeasureName(c.name) = $1
-         AND ct.tag_id = 'us.census.acs.demographics'
+         AND ct.tag_id ILIKE 'us.census%'
   $string$
   INTO measure_id
   USING standardized_name;
@@ -586,7 +633,7 @@ BEGIN
   q := q || q_select || format('FROM observatory.%I ', ((data_table_info)[1]->>'tablename'));
 
   q := format(q || ' ) ' || q_sum || ' ]::numeric[] FROM _overlaps, values
-  WHERE values.%I = _overlaps.%I', geom_geoid_colname, geom_geoid_colname);
+  WHERE values.%I = _overlaps.%I', data_geoid_colname, geom_geoid_colname);
 
   EXECUTE
     q

src/pg/sql/42_observatory_exploration.sql

@@ -114,7 +114,7 @@ BEGIN
       AND
         observatory.OBS_column.type = 'Geometry'
       AND
-        $1 && bounds::box2d
+        ST_Intersects($1, st_setsrid(observatory.obs_table.the_geom, 4326))
   $string$ || timespan_query
   USING geom;
   RETURN;

src/pg/test/expected/40_observatory_utility_test.out

@@ -27,3 +27,6 @@ t
 _obs_standardizemeasurename_test
 t
 (1 row)
+obs_dumpversion_notnull
+t
+(1 row)

src/pg/test/expected/41_observatory_augmentation_test.out

@@ -76,3 +76,15 @@ t
 obs_getuscensuscategory_polygon
 t
 (1 row)
+obs_getmeasurebyid_cartodb_census_tract
+t
+(1 row)
+obs_getmeasurebyid_null_boundary_null_timespan
+t
+(1 row)
+obs_getmeasurebyid_cartodb_block_group
+t
+(1 row)
+obs_getmeasurebyid_nulls
+t
+(1 row)

src/pg/test/expected/42_observatory_exploration_test.out

@@ -7,64 +7,9 @@ t|t
 _obs_searchtables_timespan_does_not_match
 t
 (1 row)
-id|description|name|aggregate|source
-us.census.acs.B01003001_quantile|The total number of all people living in a given geographic area.  This is a very useful catch-all denominator when calculating rates.|Quantile:Total Population|quantile|
-es.ine.total_pop|The total number of all people living in a geographic area.|Total Population|sum|
-us.census.acs.B01003001|The total number of all people living in a given geographic area.  This is a very useful catch-all denominator when calculating rates.|Total Population|sum|
-us.census.acs.B19301001|Per capita income is the mean income computed for every man, woman, and child in a particular group. It is derived by dividing the total income of a particular group by the total population.|Per Capita Income in the past 12 Months|average|
-us.census.acs.B01001001_quantile|The total number of all people living in a given geographic area.  This is a very useful catch-all denominator when calculating rates.|Quantile:Total Population|quantile|
-(5 rows)
-boundary_id|description|time_span|tablename
-us.census.tiger.zcta5|ZCTAs are approximate area representations of U.S. Postal Service (USPS) five-digit ZIP Code service areas that the Census Bureau creates using whole blocks to present statistical data from censuses and surveys. The Census Bureau defines ZCTAs by allocating each block that contains addresses to a single ZCTA, usually to the ZCTA that reflects the most frequently occurring ZIP Code for the addresses within that tabulation block. Blocks that do not contain addresses but are completely surrounded by a single ZCTA (enclaves) are assigned to the surrounding ZCTA; those surrounded by multiple ZCTAs will be added to a single ZCTA based on limited buffering performed between multiple ZCTAs. The Census Bureau identifies five-digit ZCTAs using a five-character numeric code that represents the most frequently occurring USPS ZIP Code within that ZCTA, and this code may contain leading zeros.
-There are significant changes to the 2010 ZCTA delineation from that used in 2000. Coverage was extended to include the Island Areas for 2010 so that the United States, Puerto Rico, and the Island Areas have ZCTAs. Unlike 2000, when areas that could not be assigned to a ZCTA were given a generic code ending in “XX” (land area) or “HH” (water area), for 2010 there is no universal coverage by ZCTAs, and only legitimate five-digit areas are defined. The 2010 ZCTAs will better represent the actual Zip Code service areas because the Census Bureau initiated a process before creation of 2010 blocks to add block boundaries that split polygons with large numbers of addresses using different Zip Codes.
-Data users should not use ZCTAs to identify the official USPS ZIP Code for mail delivery. The USPS makes periodic changes to ZIP Codes to support more efficient mail delivery. The ZCTAs process used primarily residential addresses and was biased towards Zip Codes used for city-style mail delivery, thus there may be Zip Codes that are primarily nonresidential or boxes only that may not have a corresponding ZCTA.|2014|obs_144e8b4f906885b2e057ac4842644a553ae49c6e
-whosonfirst.wof_continent_geom|Continents of the world.|2016|obs_9880042f935aab0d0e4b71fb6963d7726e64c534
-whosonfirst.wof_country_geom| |2016|obs_1ea93bbc109c87c676b3270789dacf7a1430db6c
-whosonfirst.wof_region_geom| |2016|obs_4fca4f060854cc3ae8c109999635a71bbde6964e
-whosonfirst.wof_marinearea_geom| |2016|obs_5105018d57c69b8a7e064fc17a9485647b311a99
-whosonfirst.wof_disputed_geom| |2016|obs_7dba9374b15fbab0c7bd7dca6dec6c4792fe86a3
-us.census.tiger.congressional_district|Congressional districts are the 435 areas from which people are elected to the U.S. House of Representatives. After the apportionment of congressional seats among the states based on decennial census population counts, each state with multiple seats is responsible for establishing congressional districts for the purpose of electing representatives. Each congressional district is to be as equal in population to all other congressional districts in a state as practicable. For the District of Columbia, Puerto Rico, and each Island Area, a separate code is used to identify the entire areas of these state-equivalent entities as having a single nonvoting delegate.|2014|obs_01b608b3a4ca503ad7acc0b1f84817bc1da3e193
-us.census.tiger.congressional_district_clipped|A cartography-ready version of US Congressional Districts|2014|obs_9d258128ff4288eb9a6d7e5c0adbef8ef0172a86
-us.census.tiger.school_district_unified_clipped|A cartography-ready version of Unified School District|2014|obs_546285f2c636f5380e7bfbb0c0db67863f6ed41d
-us.census.tiger.cbsa_clipped|A cartography-ready version of Core Based Statistical Area (CBSA)|2014|obs_3512a78ca8c7e9b5fbd5390c4ed1638b9938fcbf
-us.census.tiger.block|Census blocks are statistical areas bounded by visible features, such as streets, roads, streams, and railroad tracks, and by nonvisible boundaries, such as selected property lines and city, township, school district, and county limits and short line-of-sight extensions of streets and roads. Generally, census blocks are small in area; for example, a block in a city bounded on all sides by streets. Census blocks in suburban and rural areas may be large, irregular, and bounded by a variety of features, such as roads, streams, and transmission lines. In remote areas, census blocks may encompass hundreds of square miles. Census blocks cover the entire territory of the United States, Puerto Rico, and the Island Areas. Census blocks nest within all other tabulated census geographic entities and are the basis for all tabulated data.|2014|obs_ffebc3eb689edab4faa757f75ca02c65d7db7327
-us.census.tiger.school_district_elementary_clipped|A cartography-ready version of Elementary School District|2014|obs_41d99a86857c05f63320ea44419a68831e74ac3d
-us.census.tiger.block_group_clipped|A cartography-ready version of US Census Block Groups|2014|obs_6c1309a64d8f3e6986061f4d1ca7b57743e75e74
-us.census.tiger.census_tract_clipped|A cartography-ready version of US Census Tracts|2014|obs_fcd4e4f5610f6764973ef8c0c215b2e80bec8963
-us.census.tiger.zcta5_clipped|A cartography-ready version of US Census Zip Code Tabulation Areas|2014|obs_7615e8622a68bfc5fe37c69c9880edfb40250103
-us.census.tiger.place|Incorporated places are those reported to the Census Bureau as legally in existence as of January 1, 2010, as reported in the latest Boundary and Annexation Survey (BAS), under the laws of their respective states. An incorporated place is established to provide governmental functions for a concentration of people as opposed to a minor civil division, which generally is created to provide services or administer an area without regard, necessarily, to population. Places always are within a single state or equivalent entity, but may extend across county and county subdivision boundaries. An incorporated place usually is a city, town, village, or borough, but can have other legal descriptions. For Census Bureau data tabulation and presentation purposes, incorporated places exclude:
-Boroughs in Alaska (treated as statistical equivalents of counties).
-Towns in the New England states, New York, and Wisconsin (treated as MCDs).
-Boroughs in New York (treated as MCDs).
-Census Designated Places (CDPs) are the statistical counterparts of incorporated places, and are delineated to provide data for settled concentrations of population that are identifiable by name but are not legally incorporated under the laws of the state in which they are located. The boundaries usually are defined in cooperation with local or tribal officials and generally updated prior to each decennial census. These boundaries, which usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity boundary, have no legal status, nor do these places have officials elected to serve traditional municipal functions. CDP boundaries may change from one decennial census
-to the next with changes in the settlement pattern; a CDP with the same name as in an earlier census does not necessarily have the same boundary. CDPs must be contained within a single state and may not extend into an incorporated place. There are no population size requirements for CDPs.
-Hawaii is the only state that has no incorporated places recognized by the Census Bureau. All places shown in decennial census data products for Hawaii are CDPs. By agreement with the state of Hawaii, the Census Bureau does not show data separately for the city of Honolulu, which is coextensive with Honolulu County. In Puerto Rico, which also does not have incorporated places, the Census Bureau recognizes only CDPs and refers to them as comunidades or zonas urbanas. Guam also has only CDPs.|2014|obs_76a52df2018de8d064f1a99f93544473927cb7ae
-us.census.tiger.place_clipped|A cartography-ready version of Incorporated Places|2014|obs_db91d46d317a4ffcf509efca8e5e3a42d29e0792
-us.census.tiger.school_district_secondary_clipped|A cartography-ready version of Secondary School District|2014|obs_63b15ba742ccea136ddb88427cc37fee218702d4
-us.census.tiger.cbsa|Core Based Statistical Areas (CBSAs) consist of the county or counties or equivalent entities associated with at least one core (urbanized area or urban cluster) of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties associated with the core. The general concept of a CBSA is that of a
-core area containing a substantial population nucleus, together with adjacent communities having a high degree of economic and social integration with that core. The term “core based statistical area” became effective in 2003 and refers collectively to metropolitan statistical areas and micropolitan statistical areas. The U.S. Office of Management and Budget (OMB) defines CBSAs to provide a nationally consistent set of geographic entities for the United States and Puerto Rico for use in tabulating and presenting statistical data. Current CBSAs are based on application of the 2000 standards (published in the Federal Register of December 27, 2000) with Census 2000 data. The first set of areas defined based on the 2000 standards were announced on June 6, 2003; subsequent updates have been made to the universe of CBSAs and related statistical areas. No CBSAs are defined in the Island Areas. Statistical areas related to CBSAs include metropolitan divisions, combined statistical areas (CSAs), New England city and town areas (NECTAs), NECTA divisions, and combined NECTAs.|2014|obs_c75be9ef45e87c789c3607dd9aeef6094d5e5109
-us.census.tiger.puma_clipped|A cartography-ready version of US Census Public Use Microdata Areas|2014|obs_dc244bf520f62e4a09e290a02e55368fd0758f95
-us.census.tiger.county|The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and census areas; the latter of which are delineated cooperatively for statistical purposes by the state of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. All of the counties in Connecticut and Rhode Island and nine counties in Massachusetts were dissolved as functioning governmental entities; however, the Census Bureau continues to present data for these historical entities in order to provide comparable geographic units at the county level of the geographic hierarchy for these states and represents them as nonfunctioning legal entities in data products. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: municipios in Puerto Rico, districts and islands in American Samoa, municipalities in the Commonwealth of the Northern Mariana Islands, and islands in the U.S. Virgin Islands. Each county or statistically equivalent entity is assigned a three-character numeric Federal Information Processing Series (FIPS) code based on alphabetical sequence that is unique within state and an eight-digit National Standard feature identifier.|2014|obs_1babf5a26a1ecda5fb74963e88408f71d0364b81
-us.census.tiger.county_clipped|A cartography-ready version of US County|2014|obs_23cb5063486bd7cf36f17e89e5e65cd31b331f6e
-us.census.tiger.school_district_unified|School Districts are geographic entities within which state, county, local officials, the Bureau of Indian Affairs, or the U.S. Department of Defense provide public educational services for the area’s residents. The Census Bureau obtains the boundaries, names, local education agency codes, and school district levels for school districts from state and local school officials for the primary purpose of providing the U.S. Department of Education with estimates of the number of children “at risk” within each school district, county, and state. This information serves as the basis for the Department of Education to determine the annual allocation of Title I funding to states and school districts.
-The Census Bureau tabulates data for three types of school districts: elementary, secondary, and unified. Each school district is assigned a five-digit code that is unique within state. School district codes are the local education agency number assigned by the Department of Education and are not necessarily in alphabetical order by school district name.
-The elementary school districts provide education to the lower grade/age levels and the secondary school districts provide education to the upper grade/age levels. Unified school districts provide education to children of all school ages in their service areas. In general, where there is a unified school district, no elementary or secondary school district exists; and where there is an elementary school district, the secondary school district may or may not exist.
-The Census Bureau’s representation of school districts in various data products is based both on the grade range that a school district operates and also the grade range for which the school district is financially responsible. For example, a school district is defined as an elementary school district if its operational grade range is less than the full kindergarten through 12 or prekindergarten through 12 grade range (for example, K–6 or pre-K–8). These elementary school districts do not provide direct educational services for grades 7–12, 9–12, or similar ranges. Some elementary school districts are financially responsible for the education of all school-aged children within their service areas and rely on other school districts to provide service for those grade ranges that are not operated by these elementary school districts. In these situations, in order to allocate all school-aged children to these school districts, the secondary school district code field is blank. For elementary school districts where the operational grade range and financially responsible grade range are the same, the secondary school district code field will contain a secondary school district code. There are no situations where an elementary school district does not exist and a secondary school district exists in Census Bureau records.|2014|obs_c948b5b1e4fdaf3302d888d0dc4cc821d8857c96
-us.census.tiger.census_tract|Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity that are updated by local participants prior to each decennial census as part of the Census Bureau’s Participant Statistical Areas Program. The Census Bureau delineates census tracts in situations where no local participant existed or where state, local, or tribal governments declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of statistical data.
-Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. A census tract usually covers a contiguous area; however, the spatial size of census tracts varies widely depending on the density of settlement. Census tract boundaries are delineated with the intention of being maintained over a long time so that statistical comparisons can be made from census to census. Census tracts occasionally are split due to population growth or merged as a result of substantial population decline.
-Census tract boundaries generally follow visible and identifiable features. They may follow nonvisible legal boundaries, such as minor civil division (MCD) or incorporated place boundaries in some states and situations, to allow for census-tract-to-governmental-unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. Tribal census tracts are a unique geographic entity defined within federally recognized American Indian reservations and off-reservation trust lands and can cross state and county boundaries. Tribal census tracts may be completely different from the census tracts and block groups defined by state and county (see “Tribal Census Tract”).|2014|obs_fc050f0b8673cfe3c6aa1040f749eb40975691b7
-us.census.tiger.school_district_elementary|School Districts are geographic entities within which state, county, local officials, the Bureau of Indian Affairs, or the U.S. Department of Defense provide public educational services for the area’s residents. The Census Bureau obtains the boundaries, names, local education agency codes, and school district levels for school districts from state and local school officials for the primary purpose of providing the U.S. Department of Education with estimates of the number of children “at risk” within each school district, county, and state. This information serves as the basis for the Department of Education to determine the annual allocation of Title I funding to states and school districts.
-The Census Bureau tabulates data for three types of school districts: elementary, secondary, and unified. Each school district is assigned a five-digit code that is unique within state. School district codes are the local education agency number assigned by the Department of Education and are not necessarily in alphabetical order by school district name.
-The elementary school districts provide education to the lower grade/age levels and the secondary school districts provide education to the upper grade/age levels. Unified school districts provide education to children of all school ages in their service areas. In general, where there is a unified school district, no elementary or secondary school district exists; and where there is an elementary school district, the secondary school district may or may not exist.
-The Census Bureau’s representation of school districts in various data products is based both on the grade range that a school district operates and also the grade range for which the school district is financially responsible. For example, a school district is defined as an elementary school district if its operational grade range is less than the full kindergarten through 12 or prekindergarten through 12 grade range (for example, K–6 or pre-K–8). These elementary school districts do not provide direct educational services for grades 7–12, 9–12, or similar ranges. Some elementary school districts are financially responsible for the education of all school-aged children within their service areas and rely on other school districts to provide service for those grade ranges that are not operated by these elementary school districts. In these situations, in order to allocate all school-aged children to these school districts, the secondary school district code field is blank. For elementary school districts where the operational grade range and financially responsible grade range are the same, the secondary school district code field will contain a secondary school district code. There are no situations where an elementary school district does not exist and a secondary school district exists in Census Bureau records.|2014|obs_62ba00831d7f65309d6d663e2a99a688d336539a
-us.census.tiger.school_district_secondary|School Districts are geographic entities within which state, county, local officials, the Bureau of Indian Affairs, or the U.S. Department of Defense provide public educational services for the area’s residents. The Census Bureau obtains the boundaries, names, local education agency codes, and school district levels for school districts from state and local school officials for the primary purpose of providing the U.S. Department of Education with estimates of the number of children “at risk” within each school district, county, and state. This information serves as the basis for the Department of Education to determine the annual allocation of Title I funding to states and school districts.
-The Census Bureau tabulates data for three types of school districts: elementary, secondary, and unified. Each school district is assigned a five-digit code that is unique within state. School district codes are the local education agency number assigned by the Department of Education and are not necessarily in alphabetical order by school district name.
-The elementary school districts provide education to the lower grade/age levels and the secondary school districts provide education to the upper grade/age levels. Unified school districts provide education to children of all school ages in their service areas. In general, where there is a unified school district, no elementary or secondary school district exists; and where there is an elementary school district, the secondary school district may or may not exist.
-The Census Bureau’s representation of school districts in various data products is based both on the grade range that a school district operates and also the grade range for which the school district is financially responsible. For example, a school district is defined as an elementary school district if its operational grade range is less than the full kindergarten through 12 or prekindergarten through 12 grade range (for example, K–6 or pre-K–8). These elementary school districts do not provide direct educational services for grades 7–12, 9–12, or similar ranges. Some elementary school districts are financially responsible for the education of all school-aged children within their service areas and rely on other school districts to provide service for those grade ranges that are not operated by these elementary school districts. In these situations, in order to allocate all school-aged children to these school districts, the secondary school district code field is blank. For elementary school districts where the operational grade range and financially responsible grade range are the same, the secondary school district code field will contain a secondary school district code. There are no situations where an elementary school district does not exist and a secondary school district exists in Census Bureau records.|2014|obs_7b120df094ad943984b820d83ced7392af3a551a
-us.census.tiger.block_group|Block groups (BGs) are statistical divisions of census tracts, are generally defined to contain between 600 and 3,000 people, and are used to present data and control block numbering. A block group consists of clusters of blocks within the same census tract that have the same first digit of their four-digit census block number. For example, blocks 3001, 3002, 3003, ..., 3999 in census tract 1210.02 belong to BG 3 in that census tract. Most BGs were delineated by local participants in the Census Bureau’s Participant Statistical Areas Program. The Census Bureau delineated BGs only where a local or tribal government declined to participate, and a regional organization or State Data Center was not available to participate.
-A BG usually covers a contiguous area. Each census tract contains at least one BG, and BGs are uniquely numbered within the census tract. Within the standard census geographic hierarchy, BGs never cross state, county, or census tract boundaries but may cross the boundaries of any other geographic entity. Tribal census tracts and tribal BGs are separate and unique geographic areas defined within federally recognized American Indian reservations and can cross state and county boundaries (see “Tribal Census Tract” and “Tribal Block Group”). The tribal census tracts and tribal block groups may be completely different from the census tracts and block groups defined by state and county.|2014|obs_c6fb99c47d61289fbb8e561ff7773799d3fcc308
-us.census.tiger.state|States and Equivalent Entities are the primary governmental divisions of the United States. In addition to the 50 states, the Census Bureau treats the District of Columbia, Puerto Rico, American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands as the statistical equivalents of states for the purpose of data presentation.|2014|obs_624e5d2362e08aaa5463d7671e7748432262719c
-us.census.tiger.state_clipped|A cartography-ready version of US States|2014|obs_f39f1d7cd5a22b87140860cbd58539f1591a1810
-us.census.tiger.puma|PUMAs are geographic areas for which the Census Bureau provides selected extracts of raw data from a small sample of census records that are screened to protect confidentiality. These extracts are referred to as public use microdata sample (PUMS) files.
-For the 2010 Census, each state, the District of Columbia, Puerto Rico, and some Island Area participants delineated PUMAs for use in presenting PUMS data based on a 5 percent sample of decennial census or American Community Survey data. These areas are required to contain at least 100,000 people. This is different from Census 2000 when two types of PUMAs were defined: a 5 percent PUMA as for 2010 and an additional super-PUMA designed to provide a 1 percent sample. The PUMAs are identified by a five-digit census code unique within state.|2014|obs_7c9493c41fa8f4bd178ab993ea3d5891c1977667
-(30 rows)
+_obs_searchtotalpop
+t
+(1 row)
+_obs_getavailableboundariesexist
+t
+(1 row)

src/pg/test/fixtures/drop_fixtures.sql

@@ -6,6 +6,7 @@ DROP TABLE IF EXISTS observatory.obs_column;
 DROP TABLE IF EXISTS observatory.obs_column_tag;
 DROP TABLE IF EXISTS observatory.obs_tag;
 DROP TABLE IF EXISTS observatory.obs_column_to_column;
+DROP TABLE IF EXISTS observatory.obs_dump_version;
 DROP TABLE IF EXISTS observatory.obs_65f29658e096ca1485bf683f65fdbc9f05ec3c5d;
 DROP TABLE IF EXISTS observatory.obs_1746e37b7cd28cb131971ea4187d42d71f09c5f3;
 DROP TABLE IF EXISTS observatory.obs_1a098da56badf5f32e336002b0a81708c40d29cd;

src/pg/test/sql/40_observatory_utility_test.sql

@@ -65,14 +65,17 @@ SELECT
 
 SELECT cdb_observatory._OBS_GetRelatedColumn(
     Array[
-     'es.ine.pop_0_4',
+     'es.ine.t3_1',
      'us.census.acs.B01003001',
      'us.census.acs.B01001002'
     ],
      'denominator'
- ) = '{es.ine.total_pop,NULL,us.census.acs.B01003001}' As _OBS_GetRelatedColumn_test;
+ ) = '{es.ine.t1_1,NULL,us.census.acs.B01003001}' As _OBS_GetRelatedColumn_test;
 
 -- should give back a standardized measure name
 SELECT cdb_observatory._OBS_StandardizeMeasureName('test 343 %% 2 qqq }}{{}}') = 'test_343_2_qqq' As _OBS_StandardizeMeasureName_test;
 
+SELECT cdb_observatory.OBS_DumpVersion()
+  IS NOT NULL AS OBS_DumpVersion_notnull;
+
 \i test/fixtures/drop_fixtures.sql

src/pg/test/sql/41_observatory_augmentation_test.sql

@@ -150,7 +150,7 @@ SELECT abs(OBS_GetMeasure_total_pop_point - 10923.093200390833950) / 10923.09320
 
 -- Poly-based OBS_GetMeasure, default normalization (none)
 -- is result within 0.1% of expected
-SELECT abs(OBS_GetMeasure_total_pop_polygon - 9833.47316573952) / 9833.47316573952 < 0.001 As OBS_GetMeasure_total_pop_polygon_test FROM
+SELECT abs(OBS_GetMeasure_total_pop_polygon - 12327.3133495107) / 12327.3133495107 < 0.001 As OBS_GetMeasure_total_pop_polygon_test FROM
   cdb_observatory.OBS_GetMeasure(
     cdb_observatory._TestArea(),
     'us.census.acs.B01003001'
@@ -164,7 +164,7 @@ SELECT (abs(cdb_observatory.OBS_GetMeasure(
 -- Poly-based OBS_GetMeasure with denominator normalization
 SELECT abs(cdb_observatory.OBS_GetMeasure(
   cdb_observatory._TestArea(),
-  'us.census.acs.B01001002', 'denominator') - 0.50597531462834994530) / 0.49026340444793965457 < 0.001 As OBS_GetMeasure_total_male_poly_denominator;
+  'us.census.acs.B01001002', 'denominator') - 0.49026340444793965457) / 0.49026340444793965457 < 0.001 As OBS_GetMeasure_total_male_poly_denominator;
 
 -- Point-based OBS_GetCategory
 SELECT cdb_observatory.OBS_GetCategory(
@@ -181,7 +181,7 @@ SELECT (abs(OBS_GetPopulation - 10923.093200390833950) / 10923.093200390833950)
   ) As m(OBS_GetPopulation);
 
 -- Poly-based OBS_GetPopulation, default normalization (none)
-SELECT (abs(obs_getpopulation_polygon - 9833.47316573952) / 9833.47316573952) < 0.001 As obs_getpopulation_polygon_test
+SELECT (abs(obs_getpopulation_polygon - 12327.3133495107) / 12327.3133495107) < 0.001 As obs_getpopulation_polygon_test
 FROM
   cdb_observatory.OBS_GetPopulation(
     cdb_observatory._TestArea()
@@ -193,7 +193,7 @@ SELECT (abs(cdb_observatory.obs_getuscensusmeasure(
 
 -- Poly-based OBS_GetUSCensusMeasure, default normalization (none)
 SELECT (abs(cdb_observatory.obs_getuscensusmeasure(
-  cdb_observatory._testarea(), 'male population') - 4975.49467892449) / 4975.49467892449) < 0.001 As obs_getuscensusmeasure;
+  cdb_observatory._testarea(), 'male population') - 6043.63061042765) / 6043.63061042765) < 0.001 As obs_getuscensusmeasure;
 
 -- Point-based OBS_GetUSCensusCategory
 SELECT cdb_observatory.OBS_GetUSCensusCategory(
@@ -204,4 +204,37 @@ SELECT cdb_observatory.OBS_GetUSCensusCategory(
   cdb_observatory._testarea(), 'Spielman-Singleton Segments: 10 Clusters') = 'Low income, mix of minorities' As OBS_GetUSCensusCategory_polygon;
 
 
+-- OBS_GetMeasureById tests
+-- typical query
+SELECT (cdb_observatory.OBS_GetMeasureById(
+  '36047048500',
+  'us.census.acs.B01003001',
+  'us.census.tiger.census_tract',
+  '2010 - 2014'
+) - 3241) / 3241 < 0.0001 As OBS_GetMeasureById_cartodb_census_tract;
+
+-- no boundary_id should give null
+SELECT cdb_observatory.OBS_GetMeasureById(
+  '36047048500',
+  'us.census.acs.B01003001',
+  NULL,
+  NULL
+) IS NULL As OBS_GetMeasureById_null_boundary_null_timespan;
+
+-- query at block_group level
+SELECT (cdb_observatory.OBS_GetMeasureById(
+  '360470485002',
+  'us.census.acs.B01003001',
+  'us.census.tiger.block_group',
+  '2010 - 2014'
+) - 1900) / 1900 < 0.0001 As OBS_GetMeasureById_cartodb_block_group;
+
+-- geom ref / boundary mismatch
+SELECT cdb_observatory.OBS_GetMeasureById(
+  '36047048500',
+  'us.census.acs.B01003001',
+  'us.census.tiger.block_group',
+  '2010 - 2014'
+) IS NULL As OBS_GetMeasureById_nulls;
+
 \i test/fixtures/drop_fixtures.sql

src/pg/test/sql/42_observatory_exploration_test.sql

@@ -24,10 +24,13 @@ FROM cdb_observatory._OBS_SearchTables(
   '1988' -- year before first tiger data was collected
 ) As t(table_name, timespan);
 
-SELECT *
-FROM cdb_observatory.OBS_Search('total_pop');
+SELECT COUNT(*) > 0 AS _OBS_SearchTotalPop
+FROM cdb_observatory.OBS_Search('total_pop')
+AS t(id, description, name, aggregate, source);
 
-SELECT *
-FROM cdb_observatory.OBS_GetAvailableBoundaries(cdb_observatory._TestPoint());
+SELECT COUNT(*) > 0 AS _OBS_GetAvailableBoundariesExist
+FROM cdb_observatory.OBS_GetAvailableBoundaries(
+  cdb_observatory._TestPoint()
+) AS t(boundary_id, description, time_span, tablename);
 
 \i test/fixtures/drop_fixtures.sql