Studio 04 Geosnap and Geoprocessing

Author

Eli Knaap

Instructions

Teams
DUE: Wednesday, September 25, 2024 3:30pm

The team leader will submit a pdf version of the notebook showing all the work to answer the questions.

The first cell of the notebook should have a list of the team members (first and last names) with the team leader in bold. (Hint: Markdown cell).

Input Files

None. You will collect all data from geosnap

Reading the spatial data files

Use geosnap to collect 2017 environmental justice screening data for the San Francisco metropolitan region

(hint: the MSA fips code for the Bay Area is 41860)

from geosnap import DataStore

datasets = DataStore("/srv/data/geosnap")

# uncomment the following line and complete the code
#bay_ejscreen = gio.get_ejscreen(datasets, msa_fips="????", years=????)

Report whether the current coordinate reference system is currently projected

#bay_ejscreen.????.is_projected

Convert the GeoDataFrame into a geographic coordinate system

hint: a the standard GCS is 4326

#bay_ejscreen = bay_ejscreen.to_crs(????)

Collect highway data in the Bay Area from Open street map

(hint: first take the union of the Bay Area geometries)

#bay_union = bay_ejscreen.???_all()

#highways = ox.features_from_polygon(bay_union, tags={"highway": "????"})

Convert the Bay Area GeoDataFrame into an appropriate coordinate system

hint: a reasonable coordinate system for the Bay Area is EPSG 6419

#bay_ejscreen = bay_ejscreen.to_crs(????)

Select the Bay Area Census Blockgroups that are within 2000 meters of a highway

hint: first buffer the highway then take the intersection (ensure your geodataframes share a coordinate system before taking the intersection)

what is the median of the variable `PM25` for the blockgroups in the highway zone?

---
title: Studio 04 Geosnap and Geoprocessing
author: Eli Knaap
format:
  html:
    code-fold: false
jupyter: python3
---

## Instructions

[Teams](https://sdsu.instructure.com/courses/157862/assignments/1402793)  
**DUE: Wednesday, September 25, 2024 3:30pm** 

The team leader will submit a pdf version of the notebook showing all the work
to answer the questions.

The first cell of the notebook should have a list of the team members (first and
last names) with the team leader in bold. (Hint: Markdown cell).

## Input Files

None. You will collect all data from `geosnap`

## Reading the spatial data files

Use geosnap to collect 2017 environmental justice screening data for the San Francisco metropolitan region

(hint: the MSA fips code for the Bay Area is 41860)


```{python}
from geosnap import DataStore

datasets = DataStore("/srv/data/geosnap")

# uncomment the following line and complete the code
#bay_ejscreen = gio.get_ejscreen(datasets, msa_fips="????", years=????)
```

## Report whether the current coordinate reference system is currently projected

```{python}
#bay_ejscreen.????.is_projected
```


## Convert the GeoDataFrame into a geographic coordinate system

hint: a the standard GCS is 4326

```{python}
#bay_ejscreen = bay_ejscreen.to_crs(????)
```


## Collect highway data in the Bay Area from Open street map

(hint: first take the union of the Bay Area geometries)

```{python}
#bay_union = bay_ejscreen.???_all()

#highways = ox.features_from_polygon(bay_union, tags={"highway": "????"})
```

## Convert the Bay Area GeoDataFrame into an appropriate coordinate system

hint: a reasonable coordinate system for the Bay Area is [EPSG 6419](https://epsg.io/6419)

```{python}
#bay_ejscreen = bay_ejscreen.to_crs(????)
```


## Select the Bay Area Census Blockgroups that are within 2000 meters of a highway

hint: first buffer the highway then take the intersection (ensure your geodataframes share a coordinate system before taking the intersection)

## what is the median of the variable `PM25` for the blockgroups in the highway zone?