@hikari-murayama @chengren I was helping out one of the geopandas workshops participants briefly today on the frontdesk.... they were having trouble going through the install instructions because it was taking a VERY long time on their 2017 MacBook Air w/8GB of RAM, so we may want to double-check the install process for folks.

As an interim solution, I had them use https://datahub.berkeley.edu/ and they were able to install everything fine there, so that's something to recommend so that people can follow along even if they can't use their own laptop.

We can update this repo to add a 1-click button to get them going in the cloud while they wait for it to install on their laptops.

In the second cell of the solutions notebook 4, the following line gives an error:
schools_gdf_utm10 = schools_gdf.to_crs("epsg:26910")

ValueError: Cannot transform naive geometries. Please set a crs on the object first.

The challenge says 'Run the next cell to load a dataset containing Berkeley's bicycle boulevards (which we'll be using more in the following notebook).' but the cell with the code to load the data set is not there

When checking the results of the intersect query:

pas_utm10[parks_in_ac].head() --> parks_utm10[parks_in_ac].head()

It could be helpful to include more instruction on how to clean geospatial data. The data used in the notebooks has been carefully curated and is very clean. However, raw data is often full of errors, missing values, etc.

Currently, there are two points in the notebooks (that I can think of) where the learner encounters (and learns how to handle) common issues with geospatial data: non-matching CRS codes in Notebook 3, and typos in the bike blvds dataset in Notebook 5.

I wonder if it might be useful to also show an example of missing or incorrect geospatial data? For example, a BART station that is clearly out of place (wrong coordinates), etc. How does one identify such errors and then correct them?

This is probably not essential, but after running into some messy geo data myself, I thought it might be something to add to the workshop in the future.

In DataHub, in the import line:

ModuleNotFoundError: No module named 'solution_hider'

Repository currently does not follow D-Lab style guidelines for default branch name.

Running these notebooks on the D-Lab Datahub returns the following error:

/opt/conda/lib/python3.8/site-packages/geopandas/_compat.py:106: UserWarning: The Shapely GEOS version (3.8.0-CAPI-1.13.1 ) is incompatible with the GEOS version PyGEOS was compiled with (3.9.0-CAPI-1.16.2). Conversions between both will be slow.
 warnings.warn(

I think this requires a more recent version of shapely

Move notebooks with the prefix optional_ to a subfolder of the lessons folder

Hi,

In Chapter 9, section "8.7" (the one after 9.6), the command:
bus_routes = gpd.read_file('zip://notebook_data/transportation/Fall20Routeshape.zip')

exits with file not found.

I can't find the fie in the repo under "notebook_data/transportation" or any of the other subfolders in the repository.

I can't view the branch jupyterbook to see if it's in there.

Can you provide the file and/or update Chapter 9?

Thanks,

== todd

Hi Hikari, I just realized that the D4H stuff cannot be in a public repo. So I'm going to make this a private repo for now. Best,
Patty

1. Remove most references to py2 in 1.1 Download Anaconda
   (and remove the #windowsDownload in the https://www.anaconda.com/distribution/#windowsDownload link)
2. the conda commands in second to last section: just put the text in rather than use the geopandas manual screenshot

Notebook #7, Checking Our Output: second line of print statements needs to have .api added:

print(schools_jointracts.shape)
print(schools_gdf_api.shape) (this line here!)
print(tracts_acs_gdf_ac.shape)

Binder link is unable to load geopandas module. Could be similar to what we've previously encountered with DataHub, which appears solved now!

Error Message Below:

`ImportError Traceback (most recent call last)
in
1 import pandas as pd
----> 2 import geopandas as gpd
3
4 import matplotlib # base python plotting library
5 import matplotlib.pyplot as plt # submodule of matplotlib

/srv/conda/envs/notebook/site-packages/geopandas/init.py in
----> 1 from geopandas._config import options # noqa
2
3 from geopandas.geoseries import GeoSeries # noqa
4 from geopandas.geodataframe import GeoDataFrame # noqa
5 from geopandas.array import points_from_xy # noqa

/srv/conda/envs/notebook/site-packages/geopandas/_config.py in
124 use_pygeos = Option(
125 key="use_pygeos",
--> 126 default_value=_default_use_pygeos(),
127 doc=(
128 "Whether to use PyGEOS to speed up spatial operations. The default is True "

/srv/conda/envs/notebook/site-packages/geopandas/_config.py in _default_use_pygeos()
110
111 def _default_use_pygeos():
--> 112 import geopandas._compat as compat
113
114 return compat.USE_PYGEOS

/srv/conda/envs/notebook/site-packages/geopandas/_compat.py in
6
7 import pandas as pd
----> 8 import pyproj
9 import shapely
10 import shapely.geos

/srv/conda/envs/notebook/site-packages/pyproj/init.py in
48 import warnings
49
---> 50 from pyproj import _datadir
51 from pyproj._list import ( # noqa: F401
52 get_angular_units_map,

ImportError: cannot import name '_datadir'`

In notebook 10 at end of section 10.1, the interactive map example has a broken link (bartmap_example.html). See my screenshot attached.

• In 'read in a shapefile': 'But it remains one of the most commonly used file format for vector spatial data' --> 'But shapefiles remain one of the most commonly used file formats for vector spatial data'

• In Exploring the GeoPandas GeoDataFrame: "Notice at the end - just like we promised - a geometry column containing many numbers. Let's explore what this means, next."->"Notice at the end - just like we promised - there is a geometry column containing many numbers. Let's explore what this means next.

• In 'GeoPandas Geometries': 'In each case, coordinates are separated by a spaces, and coordinate pairs are separated by commas.' --> 'In each case, coordinates are separated by a space, and coordinate pairs are separated by commas.'

• In 'Subset the GeoDataframe': 'It looks like Alameda county is specified as "Alameda" in this dataset.' -->'It looks like Alameda County is specified as "Alameda" in this dataset.' Also the title of the section should be: 'Subset the GeoDataFrame'

• In 'Plot the GeoDataFrame': Your geodataframe may also include the variants --> Your GeoDataFrame may also include the variants

• In 'Save Your Data: Let's not forget to save out our Alameda County geodataframe alameda_county. This way we won't need to repeat the processing steps and attribute join we* did above.--> Let's not forget to save out our Alameda County GeoDataFrame alameda_county. This way we won't need to repeat the processing steps and attributejoin that we did above.

• In 'Overview': - Subsetting GeoDatFrames --> - Subsetting GeoDataFrames

Workshop Title should be "Python-Geopandas:-Parts-1-2"

In the section: Calculating Aggregated School Counts:

The next step is to group our GeoDataFrame by Census tract, and then summarize our data by group. We do this using the DataFrame method **groupy** --> groupby