New reference documentation from Pydantic

.gitignore

@@ -1,9 +1,4 @@
 .DS_Store
 .docusaurus
 node_modules/
-docs/_examples/
-docs/_schema/
-docs/schema/
-docs/schema
-static/_schema/
 build/

.markdownlint.json

@@ -0,0 +1,17 @@
+{
+  "default": true,
+  "MD001": false,
+  "MD003": false,
+  "MD010": false,
+  "MD013": false,
+  "MD024": {
+    "siblings_only": true
+  },
+  "MD025": false,
+  "MD029": false,
+  "MD033": false,
+  "MD036": false,
+  "MD040": false,
+  "MD041": false,
+  "MD046": false
+}

.prettierignore

@@ -0,0 +1,6 @@
+build/
+.docusaurus/
+node_modules/
+package-lock.json
+*.md
+*.mdx

.prettierrc

@@ -0,0 +1,10 @@
+{
+  "semi": true,
+  "singleQuote": true,
+  "tabWidth": 2,
+  "trailingComma": "es5",
+  "printWidth": 100,
+  "bracketSpacing": true,
+  "arrowParens": "always",
+  "proseWrap": "preserve"
+}

blog/2024-04-21-welcome.mdx

@@ -1,16 +1,15 @@
 ---
 title: Welcome to the Overture Maps engineering blog!
-authors:
-  name: Dana Bauer
-  title: Technical Product Manager, Overture Maps
-  email: dana@overturemaps.org
+authors: dana
 ---
 
 Welcome to the Overture Maps engineering blog!
 We're excited to tell the technical stories behind this big, exciting, ambitious, intensely collaborative project we’ve been [working on for more than a year](https://overturemaps.org/looking-forward-to-2024/).
 The engineers building Overture Maps are eager to connect with our developer community.
 In the posts we publish in the coming months, you’ll hear from team members who work at Amazon, Meta, Microsoft, TomTom, Esri, Development Seed, Precisely, and more.
+
 <!-- truncate -->
+
 And we’d like to hear from you: [feedback and contributions](https://github.com/OvertureMaps/data/discussions) from folks working with our data and schema are crucial to our success.
 Thanks for joining us on this journey.
 We’re glad you’re here.

blog/2024-04-22-beta-release.mdx

@@ -1,79 +1,84 @@
 ---
 title: Exploring our beta release
 description: Highlights from Overture Maps beta release
-authors:
-  name: Dana Bauer
-  title: Technical Product Manager, Overture Maps
-  email: dana@overturemaps.org
+authors: dana
 tags:
   - tools
   - geoparquet
 ---
+
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 
-Last week Overture Maps [announced the beta release](https://overturemaps.org/overture-maps-foundation-releases-beta-of-its-first-open-map-dataset/) of our schema and data. After months of hard work and steady improvements, we are nearing production-level stability. In a series of posts over the next few weeks -- starting with this one -- we’ll unpack the highlights and improvements you'll see in this release and beyond. 
+Last week Overture Maps [announced the beta release](https://overturemaps.org/overture-maps-foundation-releases-beta-of-its-first-open-map-dataset/) of our schema and data. After months of hard work and steady improvements, we are nearing production-level stability. In a series of posts over the next few weeks -- starting with this one -- we’ll unpack the highlights and improvements you'll see in this release and beyond.
 
 <!-- truncate -->
 
 ## Making geospatial fast
+
 The first thing you'll notice is how much faster you can [pull Overture Maps data out of the cloud](https://docs.overturemaps.org/getting-data/locally/). We made this possible by working closely with open-source collaborators on the repartitioning and [spatial optimization](https://cloudnativegeo.org/blog/2024/03/coming-soon-geoparquet-1.1/) of our GeoParquet files. Meanwhile, our friends at DuckDB, one of our favorite SQL tools, [added a feature](https://github.com/duckdb/duckdb/pull/10314) that improves the performance of queries with bounding boxes.
 
 <details>
 
-<summary>Query speeds have improved significantly from our February release to the April beta release. This example compares DuckDB queries for buildings in Philadelphia. [See here for more information about our performance testing](https://github.com/opengeospatial/geoparquet/discussions/188#discussioncomment-8909348).</summary>
+<summary>
+  Query speeds have improved significantly from our February release to the April beta release. This
+  example compares DuckDB queries for buildings in Philadelphia. [See here for more information
+  about our performance
+  testing](https://github.com/opengeospatial/geoparquet/discussions/188#discussioncomment-8909348).
+</summary>
 
     <Tabs>
         <TabItem value="february" label="2024-02-15-alpha.0 release" default>
             ```
-            SELECT 
+            SELECT
             *
-            FROM 
+            FROM
                 read_parquet('s3://overturemaps-us-west-2/release/2024-02-15-alpha.0/theme=buildings/type=building/*', filename=true, hive_partitioning=1)
-            WHERE 
+            WHERE
                 bbox.minx > -75.60154
                 AND bbox.maxx < -74.955763
                 AND bbox.miny > 39.867004
                 AND bbox.maxy < 40.137992;
             ```
-            820,915 buildings   
+            820,915 buildings
             ~120s
         </TabItem>
 
-    
+
         <TabItem value="april" label="2024-04-16-beta.0 release" default>
             ```
-            SELECT 
+            SELECT
             *
-            FROM   
+            FROM
                 read_parquet('s3://overturemaps-us-west-2/release/2024-04-16-beta.0/theme=buildings/type=building/*', filename=true, hive_partitioning=1)
-            WHERE 
+            WHERE
                 bbox.xmin > -75.60154
                 AND bbox.xmax < -74.955763
                 AND bbox.ymin > 39.867004
                 AND bbox.ymax < 40.137992
             ```
-            852,487 buildings    
+            852,487 buildings
             ~25s
         </TabItem>
     </Tabs>
+
 </details>
 
-We're continuing to make things faster and easier for users. Along with the folks at Development Seed, an [Overture Maps Foundation member](https://overturemaps.org/about/members/), we're [building special tools for Overture Maps data on top of lonboard](https://developmentseed.org/lonboard/latest/examples/overture-maps/), their Python library for visualizing large geospatial datasets in Jupyter. And recently our friends at Wherobots [took a comprehensive look](https://wherobots.com/overture-maps-data-cloud-native-geoparquet-apache-sedona/) at how our use of GeoParquet makes querying and analyzing our data with Apache Sedona very efficient. 
+We're continuing to make things faster and easier for users. Along with the folks at Development Seed, an [Overture Maps Foundation member](https://overturemaps.org/about/members/), we're [building special tools for Overture Maps data on top of lonboard](https://developmentseed.org/lonboard/latest/examples/overture-maps/), their Python library for visualizing large geospatial datasets in Jupyter. And recently our friends at Wherobots [took a comprehensive look](https://wherobots.com/overture-maps-data-cloud-native-geoparquet-apache-sedona/) at how our use of GeoParquet makes querying and analyzing our data with Apache Sedona very efficient.
 
 As you can see, we're moving forward with the community to iterate on data, software, and specifications with the shared goal of making geospatial **_fast_**.
 
 ## Easier-to-use schema
+
 Another highlight of the beta release is the transition to an easier-to-use schema for our administrative boundary data. We first [explored this idea](https://github.com/OvertureMaps/schema/discussions/117) with the Overture Maps community in February, and after two short months of work, the [new divisions schema and data](https://docs.overturemaps.org/guides/divisions/) are ready to go. Here's a query to `divisions` that grabs geometries for all the countries in the world:
 
 ```
 SELECT *
    FROM read_parquet('s3://overturemaps-us-west-2/release/2024-04-16-beta.0/theme=divisions/type=division_area/*', filename=true, hive_partitioning=1)
 WHERE subtype = 'country';
 ```
-
- You can see that the `divisions` query above is much simpler than a comparable query to `admins`:
 
+You can see that the `divisions` query above is much simpler than a comparable query to `admins`:
 
 ```
 WITH admins AS (
@@ -87,14 +92,14 @@ WITH admins AS (
       ON country_area.locality_id = country_locality.id
    WHERE country_locality.locality_type = 'country';
 
-```    
+```
 
-We plan to [deprecate admins](https://docs.overturemaps.org/release-notes/) by the July release. In the meantime, both `admins` and `divisions` will be available to users. 
+We plan to [deprecate admins](https://docs.overturemaps.org/release-notes/) by the July release. In the meantime, both `admins` and `divisions` will be available to users.
 
 ## Bridges, islands, waterfalls, and more!
+
 We added more rich detail to our `base` layer in this release, including an `infrastructure` type with familiar features from [Facebook’s Daylight map distribution](https://daylightmap.org/). We also added new subtypes and classes for the `land`, `land_use`, and `water` feature types. You'll find a comprehensive listing of the subtypes and classes for each feature type in our [schema reference docs](https://docs.overturemaps.org/schema/reference/base/infrastructure). Ready to make your own map? We have a [tutorial to help you get started](https://docs.overturemaps.org/examples/build-a-map/#13/47.6/-122.33/0/45).
-
-## Stay tuned for more highlights
-We'll be back soon with more posts that explore our path from the beta release to production. In the meantime, we invite you to [get started with our data](https://docs.overturemaps.org/getting-data/) and share with us your [comments and feedback](https://github.com/OvertureMaps/data/discussions). 
 
+## Stay tuned for more highlights
 
+We'll be back soon with more posts that explore our path from the beta release to production. In the meantime, we invite you to [get started with our data](https://docs.overturemaps.org/getting-data/) and share with us your [comments and feedback](https://github.com/OvertureMaps/data/discussions).

blog/2024-05-16-land-cover.mdx

@@ -1,9 +1,6 @@
 ---
 title: Land cover is live
-authors:
-  name: Dana Bauer
-  title: Technical Product Manager, Overture Maps
-  email: dana@overturemaps.org
+authors: dana
 tags:
   - base
 ---
@@ -16,7 +13,7 @@ import useBaseUrl from '@docusaurus/useBaseUrl';
 
 ## Mapmakers rejoice!
 
-The [May release](https://docs.overturemaps.org/release/latest) of Overture Maps includes new high-resolution land cover data *and* new cartographic schema properties.
+The [May release](https://docs.overturemaps.org/release/latest) of Overture Maps includes new high-resolution land cover data _and_ new cartographic schema properties.
 
 <!-- truncate -->
 
@@ -26,20 +23,19 @@ Our May release also includes [schema properties](https://docs.overturemaps.org/
 
 ## Exploring land cover
 
-In the notebook example below, we'll show you how to extract, process, and visualize land cover data for an area of interest using [lonboard](https://developmentseed.org/lonboard/latest/) and the [Overture Maps Python command-line tool](https://github.com/OvertureMaps/overturemaps-py). We recommend that you consult [this example](https://developmentseed.org/lonboard/latest/examples/overture-maps/ ) in the lonboard docs to better understand the methods used here. You can view and download the complete notebook on [Notebook Sharing Space](https://notebooksharing.space/view/b63f6b3dda1da99c45caf53284fbc508aaae0a43480b43bb0316db636c5e6677).
-
+In the notebook example below, we'll show you how to extract, process, and visualize land cover data for an area of interest using [lonboard](https://developmentseed.org/lonboard/latest/) and the [Overture Maps Python command-line tool](https://github.com/OvertureMaps/overturemaps-py). We recommend that you consult [this example](https://developmentseed.org/lonboard/latest/examples/overture-maps/) in the lonboard docs to better understand the methods used here. You can view and download the complete notebook on [Notebook Sharing Space](https://notebooksharing.space/view/b63f6b3dda1da99c45caf53284fbc508aaae0a43480b43bb0316db636c5e6677).
 
 ![land cover](/img/blog/notebook-overture-lc.png)
 
- To follow along, you'll need to have [JupyterLab or Jupyter Notebook](https://jupyter.org/) running and the following dependencies installed:
- - [lonboard](https://developmentseed.org/lonboard/latest/#install)
- - [overturemaps-py](https://github.com/OvertureMaps/overturemaps-py)
- - [pandas](https://pandas.pydata.org/)
- - [GeoPandas](https://geopandas.org/en/stable/index.html)
- - [Shapely](https://shapely.readthedocs.io/en/stable/index.html)
+To follow along, you'll need to have [JupyterLab or Jupyter Notebook](https://jupyter.org/) running and the following dependencies installed:
 
+- [lonboard](https://developmentseed.org/lonboard/latest/#install)
+- [overturemaps-py](https://github.com/OvertureMaps/overturemaps-py)
+- [pandas](https://pandas.pydata.org/)
+- [GeoPandas](https://geopandas.org/en/stable/index.html)
+- [Shapely](https://shapely.readthedocs.io/en/stable/index.html)
 
-``` python
+```python
 import pandas as pd
 import geopandas as gpd
 import overturemaps
@@ -48,28 +44,28 @@ from lonboard import Map, PolygonLayer
 from lonboard.colormap import apply_categorical_cmap
 ```
 
-``` python
+```python
 # specify bounding box
 bbox =  -78.6429, 39.463, -73.7806, 41.6242
 ```
 
-``` python
+```python
 # read in Overture Maps land_cover data type
 table = overturemaps.record_batch_reader("land_cover", bbox).read_all()
 table = table.combine_chunks()
 ```
 
-``` python
+```python
 # convert to dataframe
 df = table.to_pandas()
 ```
 
-``` python
+```python
 # filter for higher resolution land_cover features
 df_h = df[df.cartography.apply(lambda x: x['min_zoom'] == 8)]
 ```
 
-``` python
+```python
 # create color map for land_cover subtypes, loosely based on natural-color palette: https://www.shadedrelief.com/shelton/c.html
 color_map = {
     "urban": [167, 162, 186],
@@ -85,12 +81,12 @@ color_map = {
 }
 ```
 
-``` python
+```python
 # apply color map to land_cover subtypes
 colors = apply_categorical_cmap(df_h.subtype, color_map)
 ```
 
-``` python
+```python
 # dataframe to geodataframe, set crs
 gdf = gpd.GeoDataFrame(
     df_h,
@@ -99,7 +95,7 @@ gdf = gpd.GeoDataFrame(
 )
 ```
 
-``` python
+```python
 # create map layer
 layer = PolygonLayer.from_geopandas(
     gdf= gdf[['id','subtype', 'cartography', 'geometry']].reset_index(drop=True),
@@ -108,7 +104,7 @@ layer = PolygonLayer.from_geopandas(
 )
 ```
 
-``` python
+```python
 #render map
 view_state = {
     "longitude": -76.2,

blog/2024-07-24-explore-site.mdx

@@ -1,9 +1,6 @@
 ---
 title: Exploring Overture data, no SQL required
-authors:
-  name: Dana Bauer
-  title: Technical Product Manager, Overture Maps
-  email: dana@overturemaps.org
+authors: dana
 tags:
   - tools
 ---
@@ -17,7 +14,13 @@ Overture has launched a new browser-based tool to give users a no-code option to
     <source src={useBaseUrl('/img/blog/explore-philadelphia2.mp4')} type="video/mp4" />
   </video>
   <figcaption style={{ textAlign: 'center' }}>
-    <em>Checking out <a href="https://www.associationforpublicart.org/artwork/lion-crushing-a-serpent/">"Lion Crushing a Serpent"</a> in Philadelphia's Rittenhouse Park</em>
+    <em>
+      Checking out{' '}
+      <a href="https://www.associationforpublicart.org/artwork/lion-crushing-a-serpent/">
+        "Lion Crushing a Serpent"
+      </a>{' '}
+      in Philadelphia's Rittenhouse Park
+    </em>
   </figcaption>
 </figure>
 

blog/2024-08-15-preview-august-release.mdx

@@ -1,9 +1,6 @@
 ---
-title: What to expect in Overture’s August release
-authors:
-  name: Dana Bauer
-  title: Technical Product Manager, Overture Maps
-  email: dana@overturemaps.org
+title: What to expect in Overture's August release
+authors: dana
 tags:
   - addresses
   - base