Below I will show you as many relevant examples of my GIS, programming, and data analysis skills in use through class projects and labs. I will show pictures and videos of my work and working interactive examples where applicable. I will state what softwares, languages, and I will start with my newer and bigger stuff, where I feel like I learned the most and used a wide array of skills, and I will include earlier, more basic stuff further down the page where I find it to be relevant.
Year:
Click buttons to change displayed data.
const map = L.map('map', {
zoom: 2,
center: [30,0],
minZoom: 2,
maxZoom: 12
});
var northWest = L.latLng(90, -360),
southEast = L.latLng(-90, 360),
bounds = L.latLngBounds(northWest, southEast);
map.setMaxBounds(bounds);
const tiles = L.tileLayer('https://{s}.tile.openstreetmap.fr/hot/{z}/{x}/{y}.png', {
attribution: '© <a href="https://www.openstreetmap.org/copyright">OpenStreetMap</a> contributors',
className: 'map-tiles'
}).addTo(map);
const info = L.control();
info.onAdd = function(map){
this._div = L.DomUtil.create('div', 'info');
this.update();
return this._div;
};
var currenttime = "2023-01-01T00:00:00Z";
var selectedField = "Populati_2";
var selectedName = "Total Population";
updateMap(selectedField, selectedName, currenttime);
function onEachFeature(feature, layer) {
layer.on({
click: function(e) {
var popupcontent = '<div id="chart-container"><div id="loading-indicator">Loading...</div><canvas id="chart"></canvas></div>';
var popup = L.popup({
maxWidth: 600,
minWidth: 500,
maxHeight: 500,
minHeight: 400
}).setContent(popupcontent);
layer.bindPopup(popup).openPopup();
},
mouseover: highlightFeature,
mouseout: function() {
layer.setStyle({
color: "black",
weight: 1,
dashArray: '1',
});
info.update();
}
});
layer.on('popupopen', function() {
var valarray = [];
var timearray = [];
for (var i = 1950; i <= 2050; i++) {
timearray.push(i);
}
var clicked = String(feature.properties.Location);
console.log(clicked);
$.getJSON("https://storage.googleapis.com/jsonhost/ALL.json", function(data) {
console.log("Data loaded");
var dataarray = [];
for (var i in data) {
dataarray.push(i, data[i]);
console.log(i);
}
var lastarray = [];
valarray.push(dataarray[3]);
for (var i = 0; i < valarray[0].length; i++) {
if (valarray[0][i]['properties']['Location'] == clicked) {
lastarray.push(valarray[0][i]['properties'][selectedField]);
}
if (lastarray.length == 101) {
break;
}
}
console.log(lastarray);
console.log("Loading chart");
var chartContainer = document.getElementById('chart-container');
var loadingIndicator = document.getElementById('loading-indicator');
var chartCanvas = document.getElementById('chart');
chartCanvas.style.display = 'none';
var canvas = document.getElementById('chart');
var ctx = canvas.getContext('2d');
var chart = new Chart(ctx, {
type: 'line',
data: {
labels: timearray,
datasets: [{
data: lastarray,
label: clicked + "'s " + selectedName,
borderColor: 'blue',
fill: false,
tension: .1,
pointRadius: 0
}]
},
options: {
scales: {
y: {
title: {
display: true,
text: selectedName,
}
},
x: {
title: {
display: true,
text: "Year"
}
}
},
title: {
display: true,
text: "Test"
}
}
});
chart.update();
chartCanvas.style.display = 'block';
loadingIndicator.style.display = 'none';
popup.setContent(chartCanvas);
});
});
}
var legend = L.control({position: 'bottomright'});
function createLegend(breaks, colors){
if (map.LegendControl){
map.removeControl(map.LegendControl);
}
legend.onAdd = function(map){
var div = L.DomUtil.create('div', 'legend');
for (var i = breaks.length-1; i >= 1; i--){
div.innerHTML += '<div><i style = "background:' + colors[i] + '"></i> <b>' + breaks[i] + ' to ' + breaks[i-1] + '</b><br>';
}
if (selectedName == "Population Change"){
div.innerHTML += '<div><i style = "background:' + colors[0] + '"></i> <b>' + breaks[0] + ' to -8,618,685'+ '</b><br>';
}
else if (selectedName == "Rate of Population Change"){
div.innerHTML += '<div><i style = "background:' + colors[0] + '"></i> <b>' + breaks[0] + ' to -18.6 '+ '</b><br>';
}
else{
div.innerHTML += '<div><i style = "background:' + colors[0] + '"></i> <b>' + breaks[0] + ' to 0 '+ '</b><br>';
}
return div;
}
console.log('create legend');
legend.addTo(map);
}
var BREAKS = [39923245, 164063412, 510992617, 1078970907, 1670490596]
createLegend(BREAKS.map(number => number.toLocaleString()), ['#F0F9E8','#BAE4BC','#7BCCC4','#43A2CA','#0868AC']);
function updateMap(field, name, time){
selectedField = field;
selectedName = name;
currenttime = time;
$.getJSON("https://storage.googleapis.com/jsonhost/ALL.json", function (data){
var filtered = L.geoJSON(data,{
filter: myfilter,
onEachFeature: onEachFeature,
style: style
})
filtered.addTo(map);
info.update(data.properties);
})
}
function myfilter (feature){
if (feature.properties.Time == currenttime){
return true;
}
else{
false;
}
}
var slider = document.getElementById('mySlider');
var output = document.getElementById('sliderValue');
var timer = null;
output.innerHTML = slider.value;
slider.addEventListener('input', function(event){
clearTimeout(timer);
map.dragging.disable();
timer = setTimeout(function(){
var time = String(event.target.value);
time += "-01-01T00:00:00Z";
console.log(time);
updateMap(selectedField, selectedName, time);
}, 500);
map.dragging.enable();
})
slider.addEventListener('input', function(event){
output.innerHTML = event.target.value;
})
info.update = function (props){
var newtime = currenttime.substring(0,4);
if (selectedName == "Total Population"){
const contents = props ? `<b>${props.Location} - ${newtime}</b><br />${props[selectedField].toLocaleString()} People` : 'Hover over a country';
this._div.innerHTML = `<h4>${selectedName}</h4>${contents}`;
}
else if (selectedName == "Median Age of Population"){
const contents = props ? `<b>${props.Location} - ${newtime}</b><br />${props[selectedField].toFixed(2)} Years` : 'Hover over a country';
this._div.innerHTML = `<h4>${selectedName}</h4>${contents}`;
}
else if (selectedName == "Population Change"){
const contents = props ? `<b>${props.Location} - ${newtime}</b><br />${props[selectedField].toLocaleString()} People / yr` : 'Hover over a country';
this._div.innerHTML = `<h4>${selectedName}</h4>${contents}`;
}
else if (selectedName == "Rate of Population Change"){
const contents = props ? `<b>${props.Location} - ${newtime}</b><br />${props[selectedField].toFixed(2)}% / yr` : 'Hover over a country';
this._div.innerHTML = `<h4>${selectedName}</h4>${contents}`;
}
else if (selectedName == "Population Density"){
const contents = props ? `<b>${props.Location} - ${newtime}</b><br />${props[selectedField].toFixed(2)} People / sq. km` : 'Hover over a country';
this._div.innerHTML = `<h4>${selectedName}</h4>${contents}`;
}
else if (selectedName == "Sex Ratio of the Population"){
const contents = props ? `<b>${props.Location} - ${newtime}</b><br />${props[selectedField].toFixed(2)} Male/Female Ratio` : 'Hover over a country';
this._div.innerHTML = `<h4>${selectedName}</h4>${contents}`;
}
else if (selectedName == "Total Fertility Rate"){
const contents = props ? `<b>${props.Location} - ${newtime}</b><br />${props[selectedField].toLocaleString()} Children per Woman` : 'Hover over a country';
this._div.innerHTML = `<h4>${selectedName}</h4>${contents}`;
}
else if (selectedName == "Life Expectancy at Birth"){
const contents = props ? `<b>${props.Location} - ${newtime}</b><br />${props[selectedField].toLocaleString()} Years` : 'Hover over a country';
this._div.innerHTML = `<h4>${selectedName}</h4>${contents}`;
}
else if (selectedName == "Total Deaths"){
const contents = props ? `<b>${props.Location} - ${newtime}</b><br />${props[selectedField].toLocaleString()} People / yr` : 'Hover over a country';
this._div.innerHTML = `<h4>${selectedName}</h4>${contents}`;
}
else if (selectedName == "Crude Death Rate"){
const contents = props ? `<b>${props.Location} - ${newtime}</b><br />${props[selectedField].toLocaleString()} / 1000 People` : 'Hover over a country';
this._div.innerHTML = `<h4>${selectedName}</h4>${contents}`;
}
else if (selectedName == "Infant Mortality Rate"){
const contents = props ? `<b>${props.Location} - ${newtime}</b><br />${props[selectedField].toLocaleString()} / 1000 People` : 'Hover over a country';
this._div.innerHTML = `<h4>${selectedName}</h4>${contents}`;
}
}
info.addTo(map);
document.getElementById("button1").addEventListener("click", function() {
updateMap("Populati_2", "Total Population", currenttime);
var BREAKS = [39923245, 164063412, 510992617, 1078970907, 1670490596]
createLegend(BREAKS.map(number => number.toLocaleString()), ['#F0F9E8','#BAE4BC','#7BCCC4','#43A2CA','#0868AC']);
});
document.getElementById("button2").addEventListener("click", function() {
updateMap("Median_Pop", "Median Age of Population", currenttime);
var BREAKS = [22, 30, 40, 45, 56.68]
createLegend(BREAKS.map(number => number.toLocaleString()), ['#FEF0D9','#FDCC8A','#FC8D59','#E34A33','#B30000']);
});
document.getElementById("button3").addEventListener("click", function() {
updateMap("Population", "Population Change", currenttime);
var BREAKS = [0, 7000, 63000, 280000, 22289974]
createLegend(BREAKS.map(number => number.toLocaleString()), ['#b2182b','#f7f7f7','#d1e5f0','#67a9cf','#2166ac']);
});
document.getElementById("button4").addEventListener("click", function() {
updateMap("Rate_of_po", "Rate of Population Change", currenttime);
var BREAKS = [0, 1.00, 1.75, 2.50, 36.299]
createLegend(BREAKS.map(number => number.toFixed(2)), ['#fee08b','#ffffbf','#d9ef8b','#91cf60','#1a9850']);
});
document.getElementById("button5").addEventListener("click", function() {
updateMap("Populati_1", "Population Density", currenttime);
var BREAKS = [17.986935, 51.263107, 100.034168, 225.804202, 25278.18792]
createLegend(BREAKS.map(number => number.toFixed(2)), ['#edf8fb','#b3cde3','#8c96c6','#8856a7','#810f7c']);
});
document.getElementById("button6").addEventListener("click", function() {
updateMap("Sex_Ratio", "Sex Ratio of the Population", currenttime);
var BREAKS = [94.357655, 97.556323, 100, 102.484845, 327.456193]
createLegend(BREAKS.map(number => number.toFixed(2)), ['#E600A9','#FF73DF','#9EAAD7','#73B2FF','#0070FF']);
});
document.getElementById("button7").addEventListener("click", function() {
updateMap("TFR", "Total Fertility Rate", currenttime);
var BREAKS = [1.696023, 2.286274, 3.39118, 5.743395, 8.863675]
createLegend(BREAKS.map(number => number.toFixed(2)), ['#af8dc3','#e7d4e8','#d9f0d3','#7fbf7b','#1b7837']);
});
document.getElementById("button8").addEventListener("click", function() {
updateMap("Life_Expec", "Life Expectancy at Birth", currenttime);
var BREAKS = [57.2786, 66.7681, 72.3845, 80, 90.1865]
createLegend(BREAKS.map(number => number.toFixed(2)), ['#A63603','#E6550D','#FD8D3C','#FDBE85','#FEEDDE']);
});
document.getElementById("button9").addEventListener("click", function() {
updateMap("Total_Deat", "Total Deaths", currenttime);
var BREAKS = [231977, 803693, 1854612, 4113154, 19613435]
createLegend(BREAKS.map(number => number.toLocaleString()), ['#FFFFB2','#FECC5C','#FD8D3C','#F03B20','#BD0026']);
});
document.getElementById("button10").addEventListener("click", function() {
updateMap("Death_Rate", "Crude Death Rate", currenttime);
var BREAKS = [6.543, 8.274, 10.193, 13.524, 103.534]
createLegend(BREAKS.map(number => number.toFixed(2)), ['#FEEBE2','#FBB4B9','#F768A1','#C51B8A','#7A0177']);
});
document.getElementById("button11").addEventListener("click", function() {
updateMap("IMR", "Infant Mortality Rate", currenttime);
var BREAKS = [6.65706, 15.7782, 34.67227, 79.15877, 400.64341]
createLegend(BREAKS.map(number => number.toFixed(2)), ['#F5F500','#F5B800','#F57A00','#F53D00','#F50000']);
});
function getColor(feature, f, time){
//DONE
if (selectedName == "Total Population"){
var BREAKS = [39923245, 164063412, 510992617, 1078970907, 1670490596]
return ((f < BREAKS[0])) ? '#F0F9E8' :
((f < BREAKS[1])) ? '#BAE4BC' :
((f < BREAKS[2])) ? '#7BCCC4' :
((f < BREAKS[3])) ? '#43A2CA' :
((f < BREAKS[4])) ? '#0868AC' :
'white';
}
//DONE
else if (selectedName == "Median Age of Population"){
var BREAKS = [22, 30, 40, 45, 56.68]
return ((f < BREAKS[0])) ? '#FEF0D9' :
((f < BREAKS[1])) ? '#FDCC8A' :
((f < BREAKS[2])) ? '#FC8D59' :
((f < BREAKS[3])) ? '#E34A33' :
((f < BREAKS[4])) ? '#B30000' :
'white';
}
//Done
else if (selectedName == "Population Change"){
var BREAKS = [0, 7000, 63000, 280000, 22289974]
return ((f < BREAKS[0])) ? '#b2182b' :
((f < BREAKS[1])) ? '#f7f7f7' :
((f < BREAKS[2])) ? '#d1e5f0' :
((f < BREAKS[3])) ? '#67a9cf' :
((f < BREAKS[4])) ? '#2166ac' :
'white';
}
//Done
else if (selectedName == "Rate of Population Change"){
var BREAKS = [0, 1.00, 1.75, 2.50, 36.299]
return ((f < BREAKS[0])) ? '#fee08b' :
((f < BREAKS[1])) ? '#ffffbf' :
((f < BREAKS[2])) ? '#d9ef8b' :
((f < BREAKS[3])) ? '#91cf60' :
((f < BREAKS[4])) ? '#1a9850' :
'white';
}
//Done
else if (selectedName == "Population Density"){
var BREAKS = [17.986935, 51.263107, 100.034168, 225.804202, 25278.18792]
return ((f < BREAKS[0])) ? '#edf8fb' :
((f < BREAKS[1])) ? '#b3cde3' :
((f < BREAKS[2])) ? '#8c96c6' :
((f < BREAKS[3])) ? '#8856a7' :
((f < BREAKS[4])) ? '#810f7c' :
'white';
}
//Done
else if (selectedName == "Sex Ratio of the Population"){
var BREAKS = [94.357655, 97.556323, 100, 102.484845, 327.456193]
return ((f < BREAKS[0])) ? '#E600A9' :
((f < BREAKS[1])) ? '#FF73DF' :
((f < BREAKS[2])) ? '#9EAAD7' :
((f < BREAKS[3])) ? '#73B2FF' :
((f < BREAKS[4])) ? '#0070FF' :
'white';
}
//Done
if (selectedName == "Total Fertility Rate"){
var BREAKS = [1.696023, 2.286274, 3.39118, 5.743395, 8.863675]
return ((f < BREAKS[0])) ? '#af8dc3' :
((f < BREAKS[1])) ? '#e7d4e8' :
((f < BREAKS[2])) ? '#d9f0d3' :
((f < BREAKS[3])) ? '#7fbf7b' :
((f < BREAKS[4])) ? '#1b7837' :
'white';
}
//Done
else if (selectedName == "Life Expectancy at Birth"){
var BREAKS = [57.2786, 66.7681, 72.3845, 80, 90.1865]
return ((f < BREAKS[0])) ? '#A63603' :
((f < BREAKS[1])) ? '#E6550D' :
((f < BREAKS[2])) ? '#FD8D3C' :
((f < BREAKS[3])) ? '#FDBE85' :
((f < BREAKS[4])) ? '#FEEDDE' :
'white';
}
//Done
else if (selectedName == "Total Deaths"){
var BREAKS = [231977, 803693, 1854612, 4113154, 19613435]
return ((f < BREAKS[0])) ? '#FFFFB2' :
((f < BREAKS[1])) ? '#FECC5C' :
((f < BREAKS[2])) ? '#FD8D3C' :
((f < BREAKS[3])) ? '#F03B20' :
((f < BREAKS[4])) ? '#BD0026' :
'white';
}
//Done
else if (selectedName == "Crude Death Rate"){
var BREAKS = [6.543, 8.274, 10.193, 13.524, 103.534]
return ((f < BREAKS[0])) ? '#FEEBE2' :
((f < BREAKS[1])) ? '#FBB4B9' :
((f < BREAKS[2])) ? '#F768A1' :
((f < BREAKS[3])) ? '#C51B8A' :
((f < BREAKS[4])) ? '#7A0177' :
'white';
}
//Done
else if (selectedName == "Infant Mortality Rate"){
var BREAKS = [6.65706, 15.7782, 34.67227, 79.15877, 400.64341]
return ((f < BREAKS[0])) ? '#F5F500' :
((f < BREAKS[1])) ? '#F5B800' :
((f < BREAKS[2])) ? '#F57A00' :
((f < BREAKS[3])) ? '#F53D00' :
((f < BREAKS[4])) ? '#F50000' :
'white';
}
}
function style(feature, layer){
return {
weight: 1,
opacity: 1,
color: 'black',
dashArray: '1',
fillOpacity: 1,
fillColor: getColor(feature, feature.properties[selectedField], feature.properties.Time)
};
}
function highlightFeature(e){
const layer = e.target;
layer.setStyle({
weight: 5,
opacity: 1,
color: 'black',
dashArray: '',
fillOpacity: 1
});
layer.bringToFront();
info.update(layer.feature.properties);
}WebGIS Final Class Project: Interactive Leaflet Map
The above webmap is what I chose to do for my final project in my WebGIS class. The labs in this class covered basic CSS, HTML, JavaScript, ArcGIS Online, and Leaflet and Mapbox JavaScript APIs. The professor allowed for us to choose what we wanted to do, mainly simple one layer maps made in ArcGIS Online, story maps, basic leaflet maps, etc.. But, I didn't think that doing something that basic would truly allow me to learn more about WebGIS, and so I wanted to try to create something I would actually find interesting, complex, and beneficial.I wanted to create an interactive map using leaflet that allowed for the user to select which layer/variable they wanted displayed, and I wanted to implement a temporal scale to the data, as well as the ability to graph the data for a selected feature for the variable/layer selected. I wanted this to be implemented in a quick and easily navigated interface. A large dataset that has a large spatial AND temporal scale is U.N's demographic data. I took 11 variables and then joined them with a world countries shapefile obtained from the world bank. I then simplified the geometry to save space using mapshaper.org and converted it to a geojson file. I used this in the above map and utilized leaflet functions and JavaScript to create a way for the map's symbology to change when a different variable is selected and when the time slider is used. The information box in the top right shows the updated information for each country, and when a country is clicked on, all the years for the selected data is shown on a graph for that country.
GIS Practicum: Water and Human Resources Sustainability Analysis
My GIS Practicum involved choosing a study area to analyze water and human resources and sustainability in the chosen area. We were to create numerical models of the water inputs and outputs of the system, and create 50-year projections for a variety of variables and the resulting sustainability indices. ArcGIS Pro and ENVI were used to create a land cover raster layer, and certain groundwater recharge coefficients were assigned to each land cover, to determine what proportion of snow melt and precipitation counted as inputs to the water supply. It was also used to determine the amount of land that was developed, and a linear function was applied to this quantity to simulate an increase in developed land that was proportional to the population growth. Factors such as people per household and average household acreage were considered in this calculation. Much of the data used, such as precipitation, snow melt, and population required past data to obtain coefficients to create a 50-year projection. Using all these variables, sustainability indices for water resources and population sustainability were individually calculated. Excel was used to create projections, track data, and produce regressions. ArcGIS Pro was used for mapping and analysis, and R was used to generate the graphs shown in the above presentation.
import arcpy
import pandas as pd
import numpy as np
arcpy.env.overwriteOutput = True
class Toolbox(object):
def __init__(self):
self.label = "Toolbox"
self.alias = ""
# List of tool classes associated with this toolbox
self.tools = [AirQuality]
class AirQuality(object):
def __init__(self):
self.label = "Air Quality"
self.description = "Get the average air quality index per county"
self.canRunInBackground = False
def getParameterInfo(self):
# param0 asks the user for the input air quality csv file #
param0 = arcpy.Parameter(
displayName="Input File",
name="input_file",
datatype="DEFile",
parameterType="Required",
direction="Input"
)
# param1 asks the user to define an output project
# where the result will be exported to #
param1 = arcpy.Parameter(
displayName="Output Project",
name="output_project",
datatype="DEFile",
parameterType="Required",
direction="Output"
)
# param2 asks the user for a compatible county
# shapefile to use with the air quality data #
param2 = arcpy.Parameter(
displayName = 'County Shapefile',
name = 'shapefile',
datatype = 'GPFeatureLayer',
parameterType = 'Required',
direction = 'Input'
)
# param3 asks the user to define the geodatabase/workspace #
param3 = arcpy.Parameter(
displayName = 'Geodatabase',
name = 'geodatabase',
datatype = 'DEWorkspace',
parameterType = 'Required',
direction = 'input'
)
params = [param0, param1, param2, param3]
return params
def isLicensed(self):
"""Set whether tool is licensed to execute."""
return True
def updateParameters(self, parameters):
"""Modify the values and properties of parameters before internal
validation is performed. This method is called whenever a parameter
has been changed."""
return
def updateMessages(self, parameters):
"""Modify the messages created by internal validation for each tool
parameter. This method is called after internal validation."""
return
def execute(self, parameters, messages):
"""The source code of the tool."""
input_file = parameters[0].valueAsText
output_project = parameters[1].valueAsText
shapefile = parameters[2].valueAsText
geodatabase = parameters[3].valueAsText
data = pd.read_csv(input_file)
df = pd.DataFrame(data)
# Creates two variables that hold the average AQI values by county code and the county code values by county and prints
mean = df.groupby('COUNTY_CODE').mean()
aqi = mean['DAILY_AQI_VALUE']
code = df.groupby('COUNTY').mean()
cc = code['COUNTY_CODE']
cc = [int(x) for x in cc]
cc = [str(x) for x in cc]
for x in cc:
if len(x) == 2:
x = x.zfill(3)
else:
x = x
# Creates two numpy arrays that hold the AQI and County Code values that can be accessed by the index
length = 0
data = np.array([])
for x in aqi:
data = np.append(data, x)
length += 1
rnge = range(length)
ser = pd.Series(data, index=rnge)
data2 = np.array([])
for x in cc:
data2 = np.append(data2, x)
ser2 = pd.Series(data2, index=rnge)
row = []
# Defines the local workspace of the ArcGIS geodatabase with the county shapefile
arcpy.env.workspace = geodatabase
outpath = arcpy.env.workspace
# Creates a table in the geodatabase using the local workspace, and creates two fields for the new table
arcpy.CreateTable_management(outpath, "testTable")
arcpy.AddField_management("testTable", "COUNTYFP", "TEXT")
arcpy.AddField_management("testTable", "AVERAGE_AQI", "DOUBLE")
# Copies the table to a new table, just to make sure it is good
arcpy.CreateTable_management(outpath, "testTable2", "testTable")
# Creates a cursor that is used to find the first row in the new table
editRows = arcpy.da.InsertCursor("testTable2", ("COUNTYFP", "AVERAGE_AQI"))
# Uses a for loop to fill the empty list 'row' with County Code and Average AQI values and then inserts it into the file using the cursor
for x in rnge:
row = [ser2[x], ser[x]]
editRows.insertRow(row)
del editRows
# Creates a join between the table and the county shapefile based on the common field 'COUNTYFP'
county_joined = arcpy.AddJoin_management(shapefile, "COUNTYFP", "testTable2", "COUNTYFP")
arcpy.CopyFeatures_management(county_joined, "JoinedCounties")
# Creates the reference to the project that contains the feature layers
project = arcpy.mp.ArcGISProject(r"C:\Users\dylan\Desktop\392Project\392test\392test.aprx")
myMap = project.listMaps('Map')[0]
# Searches for the new feature layer that was just made based on its name, and then changes the symbology and creates a new output
for layer in myMap.listLayers():
if layer.isFeatureLayer == True:
if layer.name.lower() == 'joinedcounties':
symbology = layer.symbology
if hasattr(symbology, "renderer") == True:
symbology.updateRenderer('GraduatedColorsRenderer')
symbology.renderer.classificationField = "testTable2_AVERAGE_AQI"
symbology.renderer.breakCount = 5
symbology.renderer.colorRamp = project.listColorRamps('Blue-Purple (5 classes)')[0]
layer.symbology = symbology
project.saveACopy(output_project)
return
GIS Programming Project: Creation of ArcPy Toolbox to Process and Map Air Quality Data
For the final project in my GIS Programming class, me and my group chose to create an ArcPy toolbox. Me and one other were responsible for the actual programming, and the others chose the data and organized the project. The toolbox was designed to have csv files for air quality data to be input by the user. This data was then analyzed to find the average air quality index for each county (AQI) and joined with a Texas counties shapefile. The user also input the project file, and the geodatabase used. The shapefile was then symbolized using the toolbox and exported to the project file that used the specified geodatabase. The above video shows an overview of the code and the appearance and function of the toolbox.