代做GGR273 Lab 2: Environmental Conservation - Creating a Fish Stocking Web Map代写Python编程

日期：2024-08-07 08:00

GGR273 Lab 2: Environmental Conservation

Lab 2: Environmental Conservation - Creating a Fish Stocking Web Map

Due: Aug 7th 2024 @ 11:59 pm EST through the Quizzes tab

Submit through Lab 2 and answer questions and submit any links and files

Objective: Create a web map using open-source tools and data to display waterbodies stocked with fish by the Alberta public conservation authority. The final web map must include species, number stocked, and date of stocking. Your final web map will be hosted on Github.

Step 1: Setup Environment

1.   Open QGIS and Create a New Python Workbook:

o  Open QGIS, goto Plugins >

Python Console > Show Editor to create a new Python workbook.

o  The Show Editor button looks

like a little notepad with a pencil.

o  Save your workbook into the

local folder you have created for this lab. It will save as a .py file.

o  As you work through this lab and enter script, save this

workbook.

o  This is different from the python console - which is more like a live chat. In the console you can quickly execute commands. The workbook is like a journal. You can keep track of your workflow this way.

o  When you run lines of code in the workbook you will see the output in the

console. You can also test it in the console before you add it to your workbook.

o  Start by adding annotation at the top (start with #) and at the top enter your name, the date, the course code, the assignment details.

2.   Setup Python Environment:

import os

from qgis.core import QgsProject, QgsVectorLayer, QgsRasterLayer

# Set up logging import logging

logging.basicConfig(level=logging.INFO)

logging.info("Project loaded successfully")

#Define your working directory. This is the file path on your local  #computer that you have made for this project. Ensure you change any #backslashes to forward slashes

Working_directory = 'C:/path/to/your/directory'

#You can also create a working directory if yours doesn’t exist. if not os.path.exists(working_directory):

os.makedirs(working_directory)

# Set the project file path

#We are saving the .qgz file as GGR273 Lab 2, you can change the file #name to anything you want and it will save the file to your folder

project_path = os.path.join(working_directory, 'GGR273_Lab2 .qgz')

# Save the QGIS project to the specified path project = QgsProject.instance()

project.write(project_path)

# Set the working directory as the default data source location os.chdir(working_directory)

# Verify the current working directory

print(f"Current working directory: {os.getcwd()}")

# Create folders for temp files temp_dir = 'path/to/temp'

if not os.path.exists(temp_dir): os.makedirs(temp_dir)

Step 2: Import and Reproject Data

Download the geospatial data as shapefiles and the stocking report as a pdf from the following sources. Ensure they are saved in your working directory:

1.   Water Bodies Data:

o  URL:https://open.canada.ca/data/en/dataset/448ec403-6635-456b-8ced- d3ac24143add/resource/5610a875-6022-4627-ab71-c3829404c537

2.   Provincial Boundary File:

o  URL:https://www12.statcan.gc.ca/census-recensement/2021/geo/sip- pis/boundary-limites/index2021-eng.cfm?year=21

•   Download the cartography file for provinces as a shapefile

3.   Trout Planned Stocked Dates 2024 .csv:

o  Part of Quercus lab data to download: epa-fish-stocking-planned-dates- 2024_Lab2Data.csv

Additional Required Data:

Additional data will be added through plugins or created through the lab tutorial steps.

•     Basemap: An open-source web map service such as OpenStreetMap.

•     Fish Stocking Data: Extracted from the PDF and converted to CSV.

•     Attribute Data for Water Bodies: To include specific information about each water body from various sources.

These data sources will be used to complete the lab and create the final web map.

1.   Unzip shapefiles and Import Data:

import zipfile

# Unzip all .zip files in the working directory

for file_name in os.listdir(working_directory): if file_name.endswith('.zip'):

with zipfile.ZipFile(os.path.join(working_directory, file_name), 'r') as zip_ref:

zip_ref.extractall(working_directory) print(f"Extracted: {file_name}")

# Walk through the directory to find all shapefiles and #import them into QGIS for root, dirs, files in os.walk(working_directory):

for file in files:

if file.endswith('.shp'):

# Construct the full file path

file_path = os.path.join(root, file) # Create a vector layer

layer = QgsVectorLayer(file_path, os.path.basename(file_path), 'ogr')

# Check if the layer is valid if not layer.isValid():

print(f"Failed to load {file_path}") else:

# Add the layer to the project

QgsProject.instance() .addMapLayer(layer) print(f"Loaded {file_path}")

2.   Reproject Data:

o  Ensure all layers use the same CRS EPSG:3403).

EPSG:3403 refers to "NAD83 / Alberta 10-TM (Forest)". This Coordinate

Reference System (CRS) is used in Alberta, Canada, for forestry purposes. It is based on the NAD83 datum and uses a Transverse Mercator projection.

from qgis.core import QgsProject, QgsCoordinateReferenceSystem,

QgsProcessingFeatureSourceDefinition, QgsFeatureRequest, QgsVectorFileWriter

# Define the target CRS (EPSG:3403 - NAD83 / Alberta 10-TM (Forest))

target_crs = QgsCoordinateReferenceSystem('EPSG:3403')

#set the entire project CRS

project.setCrs(target_crs)

# Get the list of layers in the current project

layers = QgsProject.instance().mapLayers().values()

# Iterate over each layer and reproject for layer in layers:

# Define the parameters for reprojection params = {

'INPUT': QgsProcessingFeatureSourceDefinition(layer.id(), selectedFeaturesOnly=False, featureLimit=-1,

geometryCheck=QgsFeatureRequest.GeometryAbortOnInvalid),

'TARGET_CRS': target_crs,

'OUTPUT': 'memory:'  # Temporary layer }

# Run the reprojection algorithm

result = processing.run('native:reprojectlayer', params)

# Get the reprojected layer

reprojected_layer = result['OUTPUT']

# Set the new name for the reprojected layer

new_layer_name = layer.name() + "_reprojected" reprojected_layer.setName(new_layer_name)

# Add the reprojected layer to the project

QgsProject.instance().addMapLayer(reprojected_layer)

# Remove the original layer from the project

QgsProject.instance().removeMapLayer(layer.id())

print(f"Reprojected {layer.name()} to {target_crs.authid()} and added as {new_layer_name}")

Step 3: Update .csv of fish stocking data

1.   Normalize Species Names:

o  Convert species acronyms to full names

# Define the working directory and file paths

#set the filename to the saved .csv file from Quercus of fish stocking working_directory = 'C:/path/to/your/directory'  # Update this path

csv_file_path = os.path.join(working_directory, 'your_file.csv')

output_csv_file_path = os.path.join(working_directory, 'output_file.csv')

# Read the CSV file

df = pd.read_csv(csv_file_path)

# Define the mapping from acronyms to full names

species_mapping = {

'RNTR': 'Rainbow Trout', 'BNTR': 'Brown Trout',

'BKTR': 'Brook Trout',

'CTTR': 'Cutthroat Trout', 'TGTR': 'Tiger Trout'

}

# Convert acronyms to full names

df['Species'] = df['Species'].map(species_mapping) # Save the updated DataFrame. to a new CSV file    df.to_csv(output_csv_file_path, index=False)

# Drop columns with "Unnamed" in their name

df = df.loc[:, ~df.columns.str.contains('^Unnamed')] # Save the updated DataFrame. to a new CSV file

df.to_csv(output_csv_file_path, index=False)

# Print the data types of each column and the first three rows to verify the data

print(df.dtypes)   print(df.head(3))

Step 4: Data Preparation

1.   Select Alberta Boundaries:

First, select the Alberta boundaries from the all-Canada provinces and territories file and export it, then import it back into the QGIS project.

import os

from qgis.core import

(QgsProject,QgsVectorLayer,QgsVectorFileWriter,QgsFeatureRequest,QgsFie ld)

# Define the working directory and file paths

working_directory = 'C:/path/to/your/directory'  # Update this path

shapefile_path = os.path.join(working_directory, 'Provinces_reprojected.shp')

output_shapefile_path = os.path.join(working_directory, 'AlbertaBoundary.shp')

# Load the shapefile

layer = QgsVectorLayer(shapefile_path, 'Provinces', 'ogr') if not layer.isValid():

print("Layer failed to load!")

# Select features where PRNAME is 'Alberta'

expression = '"PRNAME" = \ 'Alberta\ ''

request = QgsFeatureRequest().setFilterExpression(expression)

alberta_features = [feat for feat in layer.getFeatures(request)]

# Check if any features were selected if len(alberta_features) == 0:

print("No features found with PRNAME 'Alberta'")

else:

# Create a new layer to store the selected features writer = QgsVectorFileWriter(

output_shapefile_path, 'UTF-8', layer.fields(),

layer.wkbType(), layer.sourceCrs(), 'ESRI Shapefile' )

# Add selected features to the new layer

for feat in alberta_features: writer.addFeature(feat)

# Delete the writer to flush and close the file del writer

# Load the new shapefile into the QGIS project

alberta_layer =

QgsVectorLayer(output_shapefile_path, 'Alberta Boundary', 'ogr')

if not alberta_layer.isValid():

print("Layer failed to load!") else:

QgsProject.instance().addMapLayer(alberta_layer) print("Layer loaded successfully!")

 

2.   Reduce Waterbodies to Provincial Boundary

a.   Test out code options to limit the waterbodies in Canada to only the water bodies in Alberta

b.   Record your code to submit later

3. Geocode the fish stocking data

from shapely.geometry

import geopandas as gpd

import Point from qgis.core import QgsVectorLayer, QgsProject

# Define the working directory and file paths

working_directory = 'C:/path/to/your/directory'  # Update this path

 

csv_file_path = os.path.join(working_directory, 'epa- fish-stocking-planned-dates-2024_Lab2Data.csv')

output_shapefile_path = os.path.join(working_directory, 'fish_stocking_points.shp')

# Read the CSV file

df = pd.read_csv(csv_file_path)

# Remove any columns named "Unnamed"

df = df.loc[:, ~df.columns.str.contains('^Unnamed')] # Create a GeoDataFrame from the CSV file using the

Latitude and Longitude columns gdf = gpd.GeoDataFrame(df,

geometry=gpd.points_from_xy(df['Longitude'], df['Latitude']))

# Set the initial CRS to WGS 84 (EPSG:4326) gdf.set_crs(epsg=4326, inplace=True)

# Reproject to UTM Zone 10N (EPSG:32610) gdf = gdf.to_crs(epsg=32610)

# Save the GeoDataFrame to a shapefile

gdf.to_file(output_shapefile_path, driver='ESRI Shapefile') # Load the new shapefile into the QGIS project

fish_stocking_layer = QgsVectorLayer(output_shapefile_path, 'Fish Stocking Points', 'ogr')

if not fish_stocking_layer.isValid():

print("Layer failed to load!") else:

QgsProject.instance().addMapLayer(fish_stocking_layer) print("Layer loaded successfully!")

Visually test your geocoding by selecting a fish stocking location and compare it to the water

bodies features. Look of the name of the lake across each file and ensure that the names of lakes are the same.

Step 6: Update Fish Stocking Variables

1. Update the Fish Stocking Planned dates data so that every record/row has a Map

Author field. Populate this field with your first and lastname as a text field. This way, for any location in your final map, your name will be attached to the data. Keep the python code you used to complete this for your final submission.

Step 7: Create Web Map

Customize Web Map in QGIS:

o  Use QGIS2Web or similar plugin for web map creation.

1.   Install qgis2web Plugin:

•     Go to Plugins > Manage and Install Plugins...

•     Search for qgis2web and install it.

2.   Update the symbology for all layers

o  Ensure fish stocking uses graduated symbols for number of fish. Consider the

skew of data and choose the appropriate classification scheme and colour scheme.

1.   You can update the number of fish stocked field in python

2.   To remove the comma from the number use the following expression in the field calculator:

CASE

WHEN "STOCKING N" IS NULL OR " STOCKING N " = '-' OR " STOCKING N " = 'TBD' THEN NULL

ELSE to_real(replace("STOCKING N ", ',', ''))

END

 

3.   Create web map

o  Go to Web > qgis2web > Create a web map.

o  Select the layers you want to include

o  You only need popups on the fish stocking report data

o  Identify the fields you want to have as popups

1.   Make sure your Fish Stocking - Map Author field is visible as a header

2.   For the rest – decide what you think makes sense, but don’t have all as headers because its too cluttered

•   Hidden Field: The field is not displayed in the pop-up.

•   Inline: The field is displayed directly within the pop-up without additional formatting.

•   Header: The field is displayed as a header within the pop-up.

•   Image: The field value is treated as a URL to an image, displaying the image in the pop-up.

•   Link: The field value is treated as a URL, displaying it as a clickable link in the pop- up.

•   Chart: Displays the field values as a chart in the pop-up.

o  Go through each tab and update the information: Appearance, Export, Settings and WIKI

o  Make sure you are choosing options that have your reader focus on identifying water bodies in Alberta, and then finding the number of fish and species of

fish that are stocked there. Any other information is less important or not important at all. The Map Author field should be highlighted and visible.

o  When youre happy with your settings, select Export

4.   Locate and View Web Map

o  Find the exported files

1.   The exported files will be saved wherever you exported them to

2.   They will be in the working directory if you didn’t save them to a different place

5.   Create a GitHub account and repository

o  The web map needs a website to host and view the web map

o  There are free websites where you can do this

o  We are going to use GitHub because it has free web hosting, version control for files, project management, accessibility, open source, and integrates with many different tools. This is also useful to add to as you create more projects and you can send as a link to potential employers or link on your cover letters for job interviews. Just make sure everything actually works!

a)   Create GitHub Repository: Create a new repository on GitHub.

o Go to https://github.com/

o Click on "Sign up" in the upper-right corner.

o Enter your email address and click "Continue".

o Create a password and continue.

o Choose a username and continue.

o Follow the remaining steps to complete the sign-up process.

b)  Upload Files: Upload the exported web map files to the repository.

o  Log in to your GitHub account.

o  Click on the + icon in the upper-right corner and select New repository.

o  Enter a repository name (e.g., Alberta-Fish-Stocking-2024).

o  Click Create repository.

o  In your repository, click on Add file > Upload files.

o  Drag and drop the exported web map files into the repository.

o  Commit the uploaded files.

c)   Enable GitHub Pages: Enable GitHub Pages in the repository settings to host your web map.

o Go to Settings in your repository.

o Scroll down to the GitHub Pages section.

o Under Source, select the branch (usually main) and the /root directory.

o Save the settings.

o GitHub will provide a URL for your hosted web map, typically https://username.github.io/repository-name/.

a)  Share URL: GitHub provides a URL that you can share to view the web map.

Ensure this works by sending to a friend and opening on different devices like your

phone. You will be submitting this link for your Lab 2 assignment. Be sure to test the pop ups.

Step 8: Answering Questions (20 points/20%): Answer the following questions based on your analysis in the Quizzes tab for Lab 2 by Aug 7th at 11:59pm EST:

1. 10% Submit your URL link on GitHub that displays your webmap.

2. 4% Submit your QGIS workbook file

3. 2% Enter the lines of code, with annotation and all of your pathways, that you used to reduce the waterbodies file to the alberta provincial boundaries

4. 2% Enter the lines of code, with annotation and all of your pathways, that you used to update   the Fish Stocking Planned dates data so that every record/row has a Map Author field. Populate this  field with your first and last name as a text field. Keep the python code you used to complete this for your final submission.

5. 1% Fill in the blank: The command to remove commas from a field value in QGIS is                  

6. 1% True/False: The expression CASE WHEN "FIELD" IS NULL THEN 0 ELSE "FIELD" END can be used to handle null values in QGIS.