Updated gitter links.

Author: metasim

CONTRIBUTING.md

@@ -71,6 +71,6 @@ and respond as necessary.
 
 Help, questions and community dialog are supported via Gitter:
 
- * https://gitter.im/s22s/raster-frames
+ * https://gitter.im/locationtech/rasterframes
 
 Commercial support is available by writing to [email protected]

README.md

@@ -1,6 +1,6 @@
 <img src="docs/src/main/paradox/_template/images/RasterFramesLogo.png" width="300px"/><sup style="vertical-align: top;">&reg;</sup>
 
- [![Join the chat at https://gitter.im/s22s/raster-frames](https://badges.gitter.im/s22s/raster-frames.svg)](https://gitter.im/s22s/raster-frames?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+ [![Join the chat at https://gitter.im/locationtech/rasterframes](https://badges.gitter.im/locationtech/rasterframes.svg)](https://gitter.im/s22s/raster-frames?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
 
 _RasterFrames™_ brings the power of Spark DataFrames to geospatial raster data, empowered by the map algebra and tile layer operations of [GeoTrellis](https://geotrellis.io/).
 

pyrasterframes/src/main/python/README.md

@@ -30,13 +30,13 @@ df.select(rf_local_add(df.tile, lit(3))).show(5, False)
 
 ## Support
 
-Reach out to us on [gitter](https://gitter.im/s22s/raster-frames)!
+Reach out to us on [gitter](https://gitter.im/s22s/locationtech/rasterframes)!
 
 Issue tracking is through [github](https://github.com/locationtech/rasterframes/issues). 
 
 ## Contributing
 
-Community contributions are always welcome. To get started, please review our [contribution guidelines](https://github.com/locationtech/rasterframes/blob/develop/CONTRIBUTING.md), [code of conduct](https://github.com/locationtech/rasterframes/blob/develop/CODE_OF_CONDUCT.md), and [developer's guide](../../../README.md).  Reach out to us on [gitter](https://gitter.im/s22s/raster-frames) so the community can help you get started!
+Community contributions are always welcome. To get started, please review our [contribution guidelines](https://github.com/locationtech/rasterframes/blob/develop/CONTRIBUTING.md), [code of conduct](https://github.com/locationtech/rasterframes/blob/develop/CODE_OF_CONDUCT.md), and [developer's guide](../../../README.md).  Reach out to us on [gitter](https://gitter.im/locationtech/rasterframes) so the community can help you get started!
 
 
 

pyrasterframes/src/main/python/docs/getting-started.pymd

@@ -52,7 +52,7 @@ You can also use RasterFrames in the following environments:
  1. Install [docker](https://docs.docker.com/install/)
  1. Pull the image: `docker pull s22s/rasterframes-notebook`
  1. Run a container with the image, for example:
-      `docker run -p 8808:8888 -p 44040:4040 -v /path/to/notebooks:/home/notebooks  rasterframes-notebook:latest`
+      `docker run -p 8808:8888 -p 44040:4040 -v /path/to/notebooks:/home/jovyan/work rasterframes-notebook:latest`
  1. In a browser, open `localhost:8808` in the example above.
 
 See [RasterFrames Notebook README](https://github.com/locationtech/rasterframes/blob/develop/rf-notebook/README.md) for instructions on building the Docker image for this Jupyter notebook server.

pyrasterframes/src/main/python/docs/index.md

@@ -24,7 +24,7 @@ The source code can be found on GitHub at [locationtech/rasterframes](https://gi
 
 ## Related Links
 
-* [Gitter Channel](https://gitter.im/s22s/raster-frames)
+* [Gitter Channel](https://gitter.im/locationtech/rasterframes)
 * [Scala API Documentation](latest/api/index.html)
 * [GitHub Repository](https://github.com/locationtech/rasterframes)
 * [Astraea, Inc.](http://www.astraea.earth/), the company behind RasterFrames

pyrasterframes/src/main/python/docs/numpy-pandas.pymd

@@ -65,7 +65,7 @@ pandas_df.iloc[0].apply(lambda v: type(v))
 
 ## User Defined Functions
 
-As we demonstrated with @ref:[vector data](vector-data.md#shapely-geometry-support), we can also make use of the `Tile` type to create [user-defined functions (UDF)](https://spark.apache.org/docs/2.3.2/api/python/pyspark.sql.html#pyspark.sql.functions.udf) that can take a _tile_ as input, return a _tile_ as output, or both. Here is a trivial and **inefficient** example of doing both. A serious performance implication of user defined functions in Python is that all the executors must move the Java objects to Python, evaluate the function, and then move the Python objects back to Java. Use the many @ref:[built-in functions](reference.md) wherever possible, and ask the [community](https://gitter.im/s22s/raster-frames) if you have an idea for a function that should be included.
+As we demonstrated with @ref:[vector data](vector-data.md#shapely-geometry-support), we can also make use of the `Tile` type to create [user-defined functions (UDF)](https://spark.apache.org/docs/2.3.2/api/python/pyspark.sql.html#pyspark.sql.functions.udf) that can take a _tile_ as input, return a _tile_ as output, or both. Here is a trivial and **inefficient** example of doing both. A serious performance implication of user defined functions in Python is that all the executors must move the Java objects to Python, evaluate the function, and then move the Python objects back to Java. Use the many @ref:[built-in functions](reference.md) wherever possible, and ask the [community](https://gitter.im/locationtech/rasterframes) if you have an idea for a function that should be included.
 
 We will demonstrate an example of creating a UDF that is logically equivalent to a built-in function. We'll quickly show that the resulting _tiles_ are approximately equivalent. The reason they are not exactly the same is that one is computed in Python and the other is computed in Java.