There are many key Esri initiatives for advancing and integrating ML methods across the platform. In his Esri Story Maps app, Mapping the Geography of Online Lending, author Jonathan Blum used the geographically weighted regression (GWR) tools in ArcGIS to explore the effect of loan grade rankings on average interest rates.
The combination of these complementary packages and technologies with the systems of record, insight, and engagement that the ArcGIS platform provides is greater than the sum of its parts. This integration empowers ArcGIS users to solve complex problems by combining powerful built-in tools with any ML package they need-from scikit-learn and TensorFlow in Python to caret in R to IBM Watson and Microsoft AI-and still benefit from spatial validation, geoenrichment, and visualization of results in ArcGIS. ArcGIS is an open, interoperable platform that allows the integration of complementary methods and techniques in several ways: through the ArcGIS API for Python, the ArcPy site package for Python, and the R-ArcGIS Bridge. The field of ML is broad, deep, and constantly evolving. The Spatially Constrained Multivariate Clustering tool uses an approach called evidence accumulation to provide the user with probabilities related to clustering results. The ordering points to identify the clustering structure (OPTICS) method in density-based clustering tools uses ML techniques to choose a cluster tolerance based on a given reachability plot.
In addition to ML methods and techniques in ArcGIS tools, ML is used throughout the ArcGIS platform for enabling smart, data-driven defaults, automating workflows, and optimizing results.įor instance, the EBK Regression Prediction method uses principal component analysis (PCA) as a means of dimension reduction to improve predictions. Examples of clustering tools in ArcGIS include Spatially Constrained Multivariate Clustering, Multivariate Clustering, Density-Based Clustering, Image Segmentation, Hot Spot Analysis, Cluster and Outlier Analysis tools, and the Space Time Pattern Mining tools. These clustering methods can be used for tasks such as segmenting school districts based on socioeconomic and demographic characteristics. ArcGIS includes a broad range of algorithms that find clusters based on one or many attributes, location, or a combination of both attributes and location. Maximum Likelihood Classification, Random Trees, and Support Vector Machine are examples of these tools.Ĭlustering groups observations based on similarities in value or location. The tools that use these methods analyze pixel values and configurations to solve problems delineating land-use types or identifying areas of forest loss. ArcGIS includes many classification methods for use on remotely sensed data. The locations of actual accidents are shown as red/yellow points.Ĭlassification determines which category an object should be assigned to based on a training dataset.
The analysis considered many factors associated with accidents: weather, time of day, speed limit, proximity to an intersection, and road characteristics. These tools can be used for tasks like estimating home values based on recent sales data and related home and community characteristics.īased on the analysis of seven years of traffic accident data, the model predicted areas with the highest risk for accidents. ArcGIS has tools for empirical Bayesian kriging (EBK), areal interpolation, EBK regression prediction, ordinary least squares (OLS) regression, OLS exploratory regression, and geographically weighted regression (GWR). ArcGIS includes regression and interpolation techniques that can be used for performing prediction analysis. Prediction uses the known to estimate the unknown. ArcGIS supports the use of ML in prediction, classification, and clustering. Both traditional and inherently spatial ML can play an important role in solving spatial problems.
The spatial component often takes the form of some measure of shape, density, contiguity, spatial distribution, or proximity. Spatial methods that incorporate some notion of geography directly into computation can lead to deeper understanding. In addition to traditional ML techniques, ArcGIS also has a subset of ML techniques that are inherently spatial. It can play a critical role in spatial problem-solving in a wide range of application areas from multivariate prediction to image classification to spatial pattern detection. ML can be computationally intensive and often involves large and complex data. ML refers to a set of data-driven algorithms and techniques that automate the prediction, classification, and clustering of data. The relationship between artificial intelligence, machine learning, and deep learning.