\n",
+ "Name: WGS 84 / Pseudo-Mercator\n",
+ "Axis Info [cartesian]:\n",
+ "- X[east]: Easting (metre)\n",
+ "- Y[north]: Northing (metre)\n",
+ "Area of Use:\n",
+ "- name: World between 85.06°S and 85.06°N.\n",
+ "- bounds: (-180.0, -85.06, 180.0, 85.06)\n",
+ "Coordinate Operation:\n",
+ "- name: Popular Visualisation Pseudo-Mercator\n",
+ "- method: Popular Visualisation Pseudo Mercator\n",
+ "Datum: World Geodetic System 1984 ensemble\n",
+ "- Ellipsoid: WGS 84\n",
+ "- Prime Meridian: Greenwich"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "counties.crs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "EPSG:3857\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(counties.crs)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Setting the CRS\n",
+ "\n",
+ "You can also set the CRS of a GeoDataFrame using the `crs` attribute. You should set the CRS if is not defined or if you think it is incorrectly defined.\n",
+ "\n",
+ "> In desktop GIS terminology, setting the CRS is called **defining the CRS**.\n",
+ "\n",
+ "As an example, let's set the CRS of our data to `None`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "# 4. Mapping and Spatial Analysis"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## More Data More Maps"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Data-Driven Mapping"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Code cells\n",
+ "\n",
+ "# Imports are always put at the top of the file. They should be on separate lines.\n",
+ "import some_package\n",
+ "\n",
+ "# But this is okay\n",
+ "from another_package import some_function\n",
+ "\n",
+ "# Comments:\n",
+ "# Use comments sparingly: better to explain in markdown!\n",
+ "# Use comments to further specify particular lines of code when needed.\n",
+ "# Comments start with a capital letter, and a space between the hashtag and the first word\n",
+ "\n",
+ "# In general ,we follow the PEP style guide: https://peps.python.org/pep-0008/\n",
+ "\n",
+ "# Coding style: \n",
+ "# Use snake case and single quotation marks, unless your string includes single quotation marks.\n",
+ "# Most operators are surrounded with a single space.\n",
+ "some_variable = 12\n",
+ "if some_variable == 10:\n",
+ " print(\"high\")\n",
+ "\n",
+ "# Always use spaces after commas\n",
+ "some_list = [1, 2, 3, 4]\n",
+ "\n",
+ "# Don’t use spaces around the = sign when used to indicate a keyword argument.\n",
+ "def some_function(a=1, b=2):\n",
+ "# Use 4 spaces per indentation level\n",
+ " print(\"something\")\n",
+ "\n",
+ "\n",
+ "# Data: \n",
+ "# use relevant social science datasets whenever you can. \n",
+ "# Think health data, demographics, etc.\n",
+ "# Stay away from \"impersonal\" datasets like the Iris or Penguins dataset.\n",
+ "# Use the top-level \"data\" folder in the repo to store data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# You might want to have an install cell, if your workshop uses a special package\n",
+ "# e.g.,\n",
+ "# !pip install [PACKAGE]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# You might want to have an import cell, so that you can do all imports at the beginning.\n",
+ "# e.g.,\n",
+ "# import numpy as np\n",
+ "# import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Subsubsection [EDIT ME]\n",
+ "\n",
+ "Use H3 headers for subsubsections, in Title Case, following [APA rules](https://apastyle.apa.org/style-grammar-guidelines/capitalization/title-case). Don't go lower than H3 if you can avoid it."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "Expandable sections
\n",
+ "Expandable sections like these can be used for hints, or for information that is optional or more advanced, and you do not want to immediately expose to all workshop participants. Use these expandable sections scarcely!\n",
+ "\n",
+ "\n",
+ "## ❗ Key Points\n",
+ "\n",
+ "* EDIT ME. Recap the most useful information in this notebook. Keep it short and use action words. Min 3, max 8.\n",
+ "* EDIT ME. Recap the most useful information in this notebook. Keep it short and use action words. Min 3, max 8.\n",
+ "* EDIT ME. Recap the most useful information in this notebook. Keep it short and use action words. Min 3, max 8.\n",
+ "\n",
+ "
"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/lessons/2_Example_R_Markdown.Rmd b/lessons/2_Example_R_Markdown.Rmd
deleted file mode 100644
index f86bc03..0000000
--- a/lessons/2_Example_R_Markdown.Rmd
+++ /dev/null
@@ -1,47 +0,0 @@
-# Workshop Name: Subtitle
-
-The beginning of every workshop notebook should be an H1 (one hashtag) header, with the workshop name, followed by a colon, followed by a subtitle for this notebook.
-
-Provide a brief introduction to what this lesson covers here.
-
-## Workshop Subtopic 1
-
-Provide some more text on what this subtopic covers.
-
-```{r}
-# You might want to have an install cell, if your workshop uses a special package
-# e.g.,
-# install.packages(PACKAGE)
-```
-
-```{r}
-# You might want to have an import cell, so that you can do all imports at the beginning.
-# e.g.,
-# library(PACKAGE)
-```
-
-Be sure to have text between code cells explaining the material, step-by-step.
-
-### Workshop Sub-subtopic 1
-
-Perhaps your subtopic will be divided into finer sub-subtopics. Be sure to use consistent headers for each one, and always have an introductory paragraph explaining what the workshop attendee can expect with this section.
-
-### Challenge #: Challenge Title Here
-
-Every challenge should be an H3 (3 hashtags) header. The challenge should be numbered, and should have a title.
-
-Insert some text here explaining the challenge.
-
-```{r}
-
-# You may have some starter code for the challenge that you can put in its own cell
-# Always have a following cell that says "YOUR CODE HERE", so that attendees know where to put their code
-```
-
-```{r challenge_X}
-# YOUR CODE HERE
-```
-
-# Optional: Closing Section
-
-You may optionally include a summary section at the end of the workshop highlighting what this notebook covered.
\ No newline at end of file
diff --git a/lessons/2_Geoprocessing and Analysis.ipynb b/lessons/2_Geoprocessing and Analysis.ipynb
new file mode 100644
index 0000000..d30cfa9
--- /dev/null
+++ b/lessons/2_Geoprocessing and Analysis.ipynb
@@ -0,0 +1,490 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Python Geospatial Fundamental: Geoprocessing and Analysis\n",
+ "\n",
+ "* * * \n",
+ "\n",
+ " \n",
+ " \n",
+ "### Learning Objectives \n",
+ " \n",
+ "* Apply more advanced Python libraries for interactive visualization.\n",
+ "* Choose domain-specific spatial datasets to create your own maps.\n",
+ "
\n",
+ "\n",
+ "### Icons Used in This Notebook\n",
+ "🔔 **Question**: A quick question to help you understand what's going on.
\n",
+ "🥊 **Challenge**: Interactive excersise. We'll work through these in the workshop!
\n",
+ "💡 **Tip**: How to do something a bit more efficiently or effectively.
\n",
+ "⚠️ **Warning:** Heads-up about tricky stuff or common mistakes.
\n",
+ "📝 **Poll:** A Zoom poll to help you learn!
\n",
+ "🎬 **Demo**: Showing off something more advanced – so you know what Python can be used for!
\n",
+ "\n",
+ "### Sections\n",
+ "1. [Mapping and Spatial Analysis](#section1)\n",
+ "2. [Interactive Visualization](#section2)\n",
+ "3. [API, Vector and Raster Data Sources](#section3)\n",
+ "4. [Create Your Own Map](#section4)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "\n",
+ "\n",
+ "# 1. Mapping and Spatial Analysis (cont'd)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Joins and Aggregation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Putting It All Together"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Be sure to have text between code cells explaining the material, step-by-step.\n",
+ "\n",
+ "Keep the following in mind:\n",
+ "- Keep narrative text limited! \n",
+ "- Avoid discussion of edge cases or caveats.\n",
+ "- Use colloquial languague and minimal jargon; if you need it, explain it.\n",
+ "- Use **boldface** to highlight key concepts and important terms, but use it sparingly.\n",
+ "- Don't use italics.\n",
+ "- Use en-dash for lists.\n",
+ "- Use `code font` for anything in code like variables, functions, and packages.\n",
+ "- Use `code font` for menu items (like `Restart Kernel`), and data frame rows/columns. \n",
+ "- When discussing functions, include the trailing parentheses (e.g. `print()`).\n",
+ "- When discussing methods, include the dot (e.g. `.sum()`).\n",
+ "- Consistently use emoji for relevant sections. See below for some examples."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "# 2. Interactive Visualization"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "# 3. API, Vector and Raster Data Sources"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "# 4. Create Your Own Map"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Code cells\n",
+ "\n",
+ "# Imports are always put at the top of the file. They should be on separate lines.\n",
+ "import some_package\n",
+ "\n",
+ "# But this is okay\n",
+ "from another_package import some_function\n",
+ "\n",
+ "# Comments:\n",
+ "# Use comments sparingly: better to explain in markdown!\n",
+ "# Use comments to further specify particular lines of code when needed.\n",
+ "# Comments start with a capital letter, and a space between the hashtag and the first word\n",
+ "\n",
+ "# In general ,we follow the PEP style guide: https://peps.python.org/pep-0008/\n",
+ "\n",
+ "# Coding style: \n",
+ "# Use snake case and single quotation marks, unless your string includes single quotation marks.\n",
+ "# Most operators are surrounded with a single space.\n",
+ "some_variable = 12\n",
+ "if some_variable == 10:\n",
+ " print(\"high\")\n",
+ "\n",
+ "# Always use spaces after commas\n",
+ "some_list = [1, 2, 3, 4]\n",
+ "\n",
+ "# Don’t use spaces around the = sign when used to indicate a keyword argument.\n",
+ "def some_function(a=1, b=2):\n",
+ "# Use 4 spaces per indentation level\n",
+ " print(\"something\")\n",
+ "\n",
+ "\n",
+ "# Data: \n",
+ "# use relevant social science datasets whenever you can. \n",
+ "# Think health data, demographics, etc.\n",
+ "# Stay away from \"impersonal\" datasets like the Iris or Penguins dataset.\n",
+ "# Use the top-level \"data\" folder in the repo to store data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# You might want to have an install cell, if your workshop uses a special package\n",
+ "# e.g.,\n",
+ "# !pip install [PACKAGE]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# You might want to have an import cell, so that you can do all imports at the beginning.\n",
+ "# e.g.,\n",
+ "# import numpy as np\n",
+ "# import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Subsubsection [EDIT ME]\n",
+ "\n",
+ "Use H3 headers for subsubsections, in Title Case, following [APA rules](https://apastyle.apa.org/style-grammar-guidelines/capitalization/title-case). Don't go lower than H3 if you can avoid it."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "jp-MarkdownHeadingCollapsed": true,
+ "tags": []
+ },
+ "source": [
+ "Expandable sections
\n",
+ "Expandable sections like these can be used for hints, or for information that is optional or more advanced, and you do not want to immediately expose to all workshop participants. Use these expandable sections scarcely!\n",
+ "\n",
+ "\n",
+ "## ❗ Key Points\n",
+ "\n",
+ "* EDIT ME. Recap the most useful information in this notebook. Keep it short and use action words. Min 3, max 8.\n",
+ "* EDIT ME. Recap the most useful information in this notebook. Keep it short and use action words. Min 3, max 8.\n",
+ "* EDIT ME. Recap the most useful information in this notebook. Keep it short and use action words. Min 3, max 8.\n",
+ "\n",
+ "
"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/lessons/1_Example_Jupyter.ipynb b/lessons/Notebook Template.ipynb
similarity index 96%
rename from lessons/1_Example_Jupyter.ipynb
rename to lessons/Notebook Template.ipynb
index 4147c95..80528ec 100644
--- a/lessons/1_Example_Jupyter.ipynb
+++ b/lessons/Notebook Template.ipynb
@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Workshop Name: Subtitle [EDIT ME]\n",
+ "# Python Geospatial Fundamental: Introduction to Geospatial Data\n",
"\n",
"* * * \n",
"\n",
@@ -12,9 +12,10 @@
" \n",
"### Learning Objectives \n",
" \n",
- "* EDIT ME. At least 3, max 6, use [learning objective verbs](https://swcarpentry.github.io/swc-releases/2016.06/instructor-training/20-lessons/).\n",
- "* EDIT ME. At least 3, max 6, use [learning objective verbs](https://swcarpentry.github.io/swc-releases/2016.06/instructor-training/20-lessons/).\n",
- "* EDIT ME. At least 3, max 6, use [learning objective verbs](https://swcarpentry.github.io/swc-releases/2016.06/instructor-training/20-lessons/).\n",
+ "* Recognize different forms of geospatial data and coordinate reference system (CRS).\n",
+ "* Use `GeoPandas` and `matplotlib` libraries to map and analyze spatial data.\n",
+ "* Apply more advanced Python libraries for interactive visualization.\n",
+ "* Choose domain-specific spatial datasets to create your own maps.\n",
"\n",
"\n",
"### Icons Used in This Notebook\n",
@@ -420,7 +421,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "Python 3",
"language": "python",
"name": "python3"
},
@@ -434,7 +435,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.9"
+ "version": "3.7.4"
}
},
"nbformat": 4,
diff --git a/solutions/01_example_jupyter_solutions.ipynb b/solutions/01_example_jupyter_solutions.ipynb
index bd51b0b..4f40dfb 100644
--- a/solutions/01_example_jupyter_solutions.ipynb
+++ b/solutions/01_example_jupyter_solutions.ipynb
@@ -112,7 +112,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.13"
+ "version": "3.11.5"
}
},
"nbformat": 4,
![](\"../images/Berkeley%20Map.jpg\")
\n",
+ "![](\"../images/UC%20Berkeley.jpg\")
\n",
+ "\n",
+ "\n",
+ "A single unit of **geographic data** includes:\n",
+ "- **Location (where)**: UC Berkeley\n",
+ "- **Attributes**: data that describe the location\n",
+ "\n",
+ "Also great to have metadata:\n",
+ "- **Source**\n",
+ "- **Datum, Projection**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![](\"../images/CRS.jpg\")
\n",
+ "![](\"../images/raster_vector.png\")
\n",
+ "![](\"https://datacarpentry.org/organization-geospatial/fig/dc-spatial-vector/pnt_line_poly.png\")
\n",
+ "\n",
+ "In the GeoDataFrame, these geometries are encoded in a format known as [Well-Known Text (WKT)](https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry). Consider the following examples:\n",
+ "\n",
+ "- POINT (30 10)\n",
+ "- LINESTRING (30 10, 10 30, 40 40)\n",
+ "- POLYGON ((30 10, 40 40, 20 40, 10 20, 30 10))\n",
+ "\n",
+ "In each case, coordinates are separated by a spaces, and coordinate pairs are separated by commas.\n",
+ "\n",
+ "Your geodataframe may also include the variants **multipoints, multilines, and multipolgyons** if the row-level feature of interest is comprised of multiple parts. For example, a GeoDataFrame of states, where one row represents one state, would have a POLYGON geometry for Utah but MULTIPOLYGON for Hawaii, which includes many islands.\n",
+ "\n",
+ "Note that, it's OK to mix and match geometries of the same family, e.g., POLYGON and MULTIPOLYGON, in the same GeoDataFrame."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's plot the Alameda County. We can specify the [color](https://matplotlib.org/stable/gallery/color/named_colors.html) of the shape. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "