ArcView and ESRI Products Cheat Sheet

Index:

ArcGIS Experience Builder

Purpose: Create web apps with no coding required.

Features: Drag-and-drop interface, responsive design, integration with ArcGIS Online (AGOL) or Portal.

Use Case: Creating a custom web app for displaying interactive maps and dashboards for urban planning.

Example:

  1. Log into AGOL.
  2. Open Experience Builder.
  3. Select a template or start from scratch.
  4. Drag and drop widgets (maps, charts, text) into the workspace.
  5. Configure data sources and interactions.
  6. Publish the web app.
from arcgis.gis import GIS
from arcgis.mapping import WebMap

gis = GIS("https://www.arcgis.com", "username", "password")
webmap_item = gis.content.get('webmap_id')
webmap = WebMap(webmap_item)

ArcGIS Online (AGOL)/Portal

Purpose: Cloud-based platform for managing and sharing GIS data and maps.

Features: Hosted services, map creation, collaboration tools.

Use Case: Sharing maps and data with stakeholders and the public, hosting web maps, and services.

Example:

  1. Log into AGOL.
  2. Create a new map.
  3. Add data layers.
  4. Configure layer properties.
  5. Save and share the map with a group or publicly.
# Publish a feature layer to AGOL
feature_layer = gis.content.add({
    'title': 'Traffic Data',
    'type': 'Feature Service',
    'tags': 'traffic, data'
}, data='C:/data/traffic_data.zip')

# Share the feature layer publicly
feature_layer.share(everyone=True)

ArcGIS Pro

Purpose: Desktop GIS application for creating, analyzing, and sharing spatial data.

Features: 2D and 3D mapping, data management, spatial analysis, integration with AGOL/Portal.

Use Case: Analyzing demographic data to determine the best location for a new retail store.

Example:

  1. Open ArcGIS Pro.
  2. Add demographic data to the project.
  3. Use spatial analysis tools to find high population density areas.
  4. Visualize results with heat maps and 3D views.
  5. Share results by publishing to AGOL.
import arcpy

# Example: Buffer analysis in ArcGIS Pro using ArcPy
input_feature = "C:/data/roads.shp"
output_feature = "C:/output/roads_buffer.shp"
buffer_distance = "100 Meters"

arcpy.Buffer_analysis(input_feature, output_feature, buffer_distance)

ArcPy

Purpose: Python site package for performing geographic data analysis, data conversion, and data management.

Features: Integration with ArcGIS Pro, script automation.

Use Case: Automating repetitive GIS tasks, such as data conversion and spatial analysis.

Example:

  1. Write a Python script to automate data processing.
  2. Use ArcPy functions for spatial analysis.
  3. Schedule the script to run periodically using Windows Task Scheduler.
import arcpy

# Automate data processing
input_fc = "C:/data/land_use.shp"
output_fc = "C:/output/land_use_analysis.shp"

# Perform spatial join
arcpy.analysis.SpatialJoin(target_features=input_fc, join_features="C:/data/zoning.shp", out_feature_class=output_fc)

ArcGIS for Server

Purpose: Host and manage GIS services on your own servers.

Features: Publish, manage, and secure web services.

Use Case: Serving map and feature services to web apps for real-time data updates.

Example:

  1. Publish a map service using ArcGIS Pro or ArcMap.
  2. Configure the service settings in ArcGIS Server Manager.
  3. Use the service URL in web apps to access the data.
# Example: Publishing a service using ArcPy
service_name = "MyMapService"
sddraft = "C:/data/MyMapService.sddraft"
sd = "C:/data/MyMapService.sd"

arcpy.mapping.CreateMapSDDraft("C:/data/my_map.mxd", sddraft, service_name, 'ARCGIS_SERVER')
arcpy.StageService_server(sddraft, sd)
arcpy.UploadServiceDefinition_server(sd, "My Hosting Server")

ArcGIS for Portal

Purpose: On-premises equivalent of ArcGIS Online.

Features: Secure sharing of maps and data within an organization.

Use Case: Internal sharing of sensitive GIS data and maps within an enterprise.

Example:

  1. Install and configure ArcGIS Enterprise.
  2. Publish data to the Portal.
  3. Create and share web maps and apps within the organization.
from arcgis.gis import GIS

# Connect to ArcGIS Enterprise Portal
gis = GIS("https://your_portal_url", "username", "password")

# Share an existing item within the organization
item = gis.content.get('item_id')
item.share(org=True)

Python Integration

Platform: Anaconda for managing Python environments, Visual Studio Code (VSC) for development.

Packages: arcpy, arcgis (ArcGIS API for Python).

Example: Environment Setup

# Create a new conda environment
conda create -n gis_env python=3.8

# Activate the environment
conda activate gis_env

# Install ArcGIS API for Python
conda install -c esri arcgis

Example: Using ArcGIS API for Python

from arcgis.gis import GIS

# Connect to AGOL
gis = GIS("https://www.arcgis.com", "username", "password")

# Search for content
content = gis.content.search("land cover", item_type="Feature Layer")
for item in content:
    print(item.title)

AI Integration Example

Use Case: Enhancing spatial analysis with AI, such as predicting traffic congestion.

import openai

# Example: Using OpenAI API for predictive analysis
openai.api_key = 'your_openai_api_key'

def predict_traffic(data):
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=f"Predict traffic congestion based on the following data: {data}",
        max_tokens=100
    )
    return response.choices[0].text.strip()

# Sample traffic data
traffic_data = {
    'time': '08:00',
    'location': 'Downtown',
    'weather': 'Clear',
    'day': 'Monday'
}

prediction = predict_traffic(traffic_data)
print(f"Traffic Prediction: {prediction}")

Integration with FME (Feature Manipulation Engine)

Purpose: Data integration platform for transforming and integrating spatial data.

Use Case: Automating ETL (Extract, Transform, Load) processes between different GIS and non-GIS data sources.

Example:

  1. Open FME Workbench.
  2. Create a new workspace.
  3. Add readers (input data sources) and writers (output data destinations).
  4. Use transformers to manipulate the data as needed.
  5. Save and run the workspace.
# Example: Running an FME workspace using Python
import fmeobjects

fme_workspace = "C:/data/my_workspace.fmw"
fme_session = fmeobjects.FMEWorkspaceRunner()
fme_session.runWithParameters(fme_workspace, {"SourceDataset": "C:/data/input.shp", "DestDataset": "C:/output/output.shp"})

Integration with R

Purpose: Statistical computing and graphics.

Use Case: Performing advanced statistical analysis and visualizations on GIS data.

Example:

  1. Install R and RStudio.
  2. Install necessary packages (sf for spatial data, ggplot2 for visualization).
  3. Import GIS data into R for analysis.
  4. Perform statistical analysis and create visualizations.
# Example: Analyzing GIS data in R
install.packages("sf")
install.packages("ggplot2")

library(sf)
library(ggplot2)

# Read shapefile
shapefile <- st_read("C:/data/land_use.shp")

# Plot the data
ggplot(data = shapefile) +
  geom_sf() +
  ggtitle("Land Use Data")

Integration with SQL Server

Purpose: Managing and storing spatial data in a relational database.

Use Case: Storing large datasets and performing spatial queries.

Example:

  1. Install SQL Server and enable spatial features.
  2. Create a spatial database.
  3. Load spatial data into the database.
  4. Perform spatial queries using T-SQL.
-- Example: Creating a spatial table and performing a spatial query in SQL Server
CREATE TABLE LandUse (
    Id INT PRIMARY KEY,
    Name NVARCHAR(100),
    Shape GEOMETRY
);

-- Insert spatial data
INSERT INTO LandUse (Id, Name, Shape)
VALUES (1, 'Park', geometry::STGeomFromText('POLYGON ((...))', 4326));

-- Perform a spatial query
SELECT Name
FROM LandUse
WHERE Shape.STIntersects(geometry::STGeomFromText('POINT(...)', 4326)) = 1;

Scenario: Using ArcGIS Products in a Project

Project: Analyzing traffic patterns for city planning.

Project Overview:

The city planning department has initiated a project to analyze traffic patterns within the urban area to identify high traffic zones, predict future traffic congestion, and develop strategies for traffic management. The goal is to use GIS tools to collect, process, analyze, and visualize traffic data, and provide actionable insights to city planners and decision-makers.

Analyst Task:

As a GIS Analyst, your task is to use a suite of ArcGIS products and integration tools to achieve the following:

  1. Collect and preprocess traffic data from various sources.
  2. Perform spatial analysis to identify high traffic areas.
  3. Automate data processing tasks to ensure efficiency and accuracy.
  4. Publish traffic data as web services for accessibility.
  5. Create and share interactive web maps with stakeholders.
  6. Develop a public-facing web app for displaying live traffic updates.
  7. Integrate AI for predictive analysis of future traffic patterns.
  8. Automate ETL processes using FME for seamless data integration.
  9. Conduct advanced statistical analysis using R for deeper insights.
  10. Store and query traffic data in a SQL Server database for robust data management.

ArcGIS Pro: Perform spatial analysis to identify high traffic areas.

  1. Open ArcGIS Pro and create a new project.
  2. Import traffic data (e.g., shapefiles, CSV files).
  3. Use spatial analysis tools such as Kernel Density or Hot Spot Analysis to identify high traffic areas.
  4. Visualize the results using heat maps or 3D views.
  5. Save the project and export maps as needed.
import arcpy

# Example: Kernel Density analysis in ArcGIS Pro using ArcPy
input_feature = "C:/data/traffic_accidents.shp"
output_raster = "C:/output/traffic_density.tif"
cell_size = 30

arcpy.sa.KernelDensity(input_feature, "NONE", cell_size, output_raster)

ArcPy: Automate data processing tasks, such as merging traffic data from multiple sources.

  1. Write a Python script using ArcPy to automate data processing.
  2. Merge traffic data from multiple sources.
  3. Perform any necessary data cleaning and transformation.
  4. Save the processed data to a new file.
import arcpy

# Automate data processing
input_features = ["C:/data/traffic_2022.shp", "C:/data/traffic_2023.shp"]
output_feature = "C:/output/merged_traffic.shp"

arcpy.Merge_management(inputs=input_features, output=output_feature)

# Clean and transform data
arcpy.AddField_management(output_feature, "Cleaned", "TEXT")
arcpy.CalculateField_management(output_feature, "Cleaned", '"YES"', "PYTHON3")

ArcGIS for Server: Publish traffic data as a web service.

  1. Publish the traffic data as a map or feature service using ArcGIS Pro.
  2. Configure the service settings in ArcGIS Server Manager.
  3. Set up permissions and security settings.
  4. Use the service URL to access the data in web applications.
# Example: Publishing a service using ArcPy
service_name = "TrafficDataService"
sddraft = "C:/data/TrafficDataService.sddraft"
sd = "C:/data/TrafficDataService.sd"

arcpy.mapping.CreateMapSDDraft("C:/data/traffic_data.mxd", sddraft, service_name, 'ARCGIS_SERVER')
arcpy.StageService_server(sddraft, sd)
arcpy.UploadServiceDefinition_server(sd, "My Hosting Server")

ArcGIS Online: Create a web map to visualize traffic patterns and share with city planners.

  1. Log into ArcGIS Online.
  2. Create a new web map.
  3. Add the published traffic data service to the map.
  4. Configure layer properties and symbology.
  5. Save the map and share it with city planners and stakeholders.
from arcgis.gis import GIS

gis = GIS("https://www.arcgis.com", "username", "password")
webmap = gis.map('City Name')

# Add traffic data layer
traffic_layer = gis.content.search("TrafficDataService", item_type="Feature Layer")[0]
webmap.add_layer(traffic_layer)

# Save and share the web map
webmap.save({
    "title": "Traffic Patterns Map",
    "tags": ["traffic", "city planning"],
    "snippet": "Web map showing traffic patterns in the city."
})
webmap.share(everyone=True)

Experience Builder: Develop a web app for public use, showing live traffic updates and analysis results.

  1. Open ArcGIS Experience Builder.
  2. Select a template or start from scratch.
  3. Add maps, charts, and other widgets to display traffic data and analysis results.
  4. Configure data sources and interactions.
  5. Publish the web app for public use.

AI Integration: Use OpenAI API to predict future traffic patterns based on current data.

  1. Collect current traffic data.
  2. Use OpenAI API to analyze the data and predict future traffic patterns.
  3. Integrate the predictions into the web map or app.
import openai

openai.api_key = 'your_openai_api_key'

def predict_traffic(data):
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=f"Predict traffic congestion based on the following data: {data}",
        max_tokens=100
    )
    return response.choices[0].text.strip()

# Sample traffic data
traffic_data = {
    'time': '08:00',
    'location': 'Downtown',
    'weather': 'Clear',
    'day': 'Monday'
}

prediction = predict_traffic(traffic_data)
print(f"Traffic Prediction: {prediction}")

FME Integration: Automate data transformation and integration processes.

  1. Open FME Workbench and create a new workspace.
  2. Add readers for input traffic data and writers for output data destinations.
  3. Use transformers to clean and transform the data.
  4. Save and run the workspace to automate the ETL process.
import fmeobjects

fme_workspace = "C:/data/traffic_workspace.fmw"
fme_session = fmeobjects.FMEWorkspaceRunner()
fme_session.runWithParameters(fme_workspace, {"SourceDataset": "C:/data/input.shp", "DestDataset": "C:/output/output.shp"})

R Integration: Perform advanced statistical analysis on traffic data.

  1. Install R and RStudio.
  2. Install necessary R packages (sf, ggplot2).
  3. Import traffic data into R for analysis.
  4. Perform statistical analysis and create visualizations.
install.packages("sf")
install.packages("ggplot2")

library(sf)
library(ggplot2)

# Read shapefile
traffic_data <- st_read("C:/data/traffic.shp")

# Plot the data
ggplot(data = traffic_data) +
  geom_sf() +
  ggtitle("Traffic Data Analysis")

SQL Server Integration: Store and query traffic data in a spatial database.

  1. Install SQL Server and enable spatial features.
  2. Create a spatial database and tables.
  3. Load traffic data into the database.
  4. Perform spatial queries to analyze traffic data.
-- Example: Creating a spatial table and performing a spatial query in SQL Server
CREATE TABLE TrafficData (
    Id INT PRIMARY KEY,
    Location NVARCHAR(100),
    Shape GEOMETRY
);

-- Insert spatial data
INSERT INTO TrafficData (Id, Location, Shape)
VALUES (1, 'Downtown', geometry::STGeomFromText('POINT (30 10)', 4326));

-- Perform a spatial query
SELECT Location
FROM TrafficData
WHERE Shape.STIntersects(geometry::STGeomFromText('POINT (30 10)', 4326)) = 1;