ArcGIS mapping and analytics software allows you to visualise and analyse your data in terms of its location on the map. You can enhance your data with content from Esri's collection of global geographic information. Then, use ArcGIS to take accurate measurements, spot patterns, and identify relationships among features. Plan efficient routes, perform profitable site selection and model predictions to make better decisions.
Perform analytics using your data and data from Esri, all inside ArcGIS
All data formats and sizes
Analyse streams of real-time data, making full use of your IoT investments. Uncover knowledge hidden beneath the surface. Analyse traditional vector and tabular data—from the miniscule to the massive. Process raster and imagery data using state-of-the-art techniques at any scale.
Data from multiple sources
With ArcGIS, it’s easy to integrate information from many sources and locations. Add local data to your organisation’s authoritative data. Include dynamic and cloud-based data such as demographics, connect to live feeds from social media, and add other content from the ArcGIS Living Atlas of the World.
Global geographic data
Access a wide range of ready-to-use maps and data from the ArcGIS Living Atlas of the World. This foremost collection of global geographic content is curated and updated by Esri and its partners. You can use the maps and data as is, or combine them with your own data to gain deeper understanding.
Your map provides data visualisation for powerful analytics
When you geocode your data, you place it accurately on a map and symbolise it for further understanding. Using ArcGIS, you will be able to answer questions such as, “Where are our regional offices located?” and “Where are all of our delivery trucks?”
Create 2D and 3D maps to see exact locations. Display real-time positions of moving vehicles. Watch events as they unfold. Use continuous visualisation with real-time monitoring and alerts. You will be able to better understand changing patterns and situations.
ArcGIS includes graphic tools, such as charts and tables. Use them to quickly discover patterns, trends or outliers and to find and disclose information that would have been otherwise indiscernible.
Find dimensions and distribution
Find dimensions and distribution of features at a location
Determine feature dimensions
Asking questions of our data often means taking measurements. You may need to measure the size of an individual feature. Find the dimensions of features such as the height of a building, the slope of a bike path, the volume of a lake, or the length of a river. Or, find the spatial fingerprint of a complete set of features.
Summarise data within a location
Identify the distribution of features at a location. For example, determine the total number of schools within a city or the number of rivers within ten miles of a pipeline. And, see how many customers live within five miles of your store.
Describe and quantify relationships between features at a location
Detect overlapping relationships in space and time
Relationships among features include associations such as proximity, coincidence, intersection, overlap, visibility, and accessibility. Find the intersection of objects in space and time using temporal queries and visualisation. Answer questions such as, “When and where will whale migration paths intersect with maritime shipping routes?”
Determine visibility between locations
Identify which features are visible from a given location. Consider terrain, physical objects or obstructions, and observer/target properties. Answer questions such as, “What buildings have a direct line of sight to a new radio tower?” or “Are the forest timber harvests visible from the scenic corridor?”
Find best routes and sites
Navigate and select sites to meet your unique needs
Find sites that meet your criteria
Identify which features you need in a location. Use site selection tools to prioritise and filter for ideal locations. For example, find the best place for a new coffee shop according to requirements such as demographics, traffic, and accessibility.
Select locations based on landscape
Locate a suitable landscape such as a slope range or level of sunlight exposure. Use built-in location-allocation principles. Find locations that minimise the distance or reduce the cost between points of supply and demand.
Design routes that meet a set of criteria
Find the quickest, shortest, or even the most scenic routes for an entire fleet. Calculate drive-times and locate facilities. Include cost values such as distance, time, slope, or other flow attributes. Solve for just two stop locations or sequence many stops in order.
Detect and quantify patterns
Go beyond visualisation to analyse your data
Identify hot spots, cold spots, and outliers
Detect statistically significant hotspots, cold spots, and outliers. Make decisions based on these observed patterns. Answer questions such as, “Where are clusters of high expenditures on electronic goods?” or “Where are the hot spots of cancer deaths?”
Detect natural groups or clusters
Find clusters in your data by combining location information with multiple variables. For example, find areas with similar vulnerability characteristics based on socioeconomic status, governance, population density, and climate change.
Identify change over time
Gain additional knowledge by adding temporal data. Analyse how spatial patterns (such as hotspots, low spots, clusters and outliers) have changed in both time and space. Answer question based on what you see: “Are rich and poor communities becoming clustered?” or “Have pine beetle outbreak hotspots grown larger or smaller?”
Predictions based on data, machine learning, and proven techniques
Predict success with benchmarking techniques
Bring in data such as population, demographics, and market potential. Determine which locations are likely to offer a return on investment. Predict which locations are more likely to fail. Use benchmarking techniques. Answer questions such as, “How would a potential store perform based on its similarity to another successful store?"
Assess location values using regression analysis
Find factors that explain observed spatial patterns by using regression approaches. Answer questions such as, “What factors contribute to people dying young?” or “Where should we focus intervention?” Use this analysis to make predictions and implement effective policies.
Estimate location values with sample data
Predict values for a location based on discrete sample observations. Quantify the certainty of your analysis results. For example, given a set of oil well production points across an area you can determine the estimated production values in previously unmeasured locations.