Spatial data science is the science of extracting insights from data by leveraging a comprehensive set of spatialalgorithmsand analytical methods. Often,machine learningalgorithms anddeep learningnetworks are used to identify hidden patterns within the data to create or improvepredictive modelsfor a range...
This is accomplished by leveraging myriad data sources (data from IoT sensors, 3D models, etc.), sophisticated analytics, and 3D-location data made possible with computer vision, volumetric cameras, and more. Spatial computing contextualizes data for a three-dimensional world. What are the benefits...
Gather data.Spatial mapping techniques are used to gather data around the user and the device's surroundings. Techniques such as photogrammetry,lidar and radar capture a 3D modelof the world. Lidar or radar measure the reflection of a laser or radio signal off objects around a scanner to autom...
An alternative is to explore new models, new objective functions, and new patterns which are more suitable for spatial data and their unique properties. This chapter investigates techniques in the literature to incorporate spatial components via feature selection, new models, new objective functions, ...
exercisesin which the models are plainly absurd. The point of the exercises, however, is not so much the detection of absurd models as understanding the sources ofabsurdity. From a more practical viewpoint, the more interesting exercises are those in which the absurdity is not obvious at first...
Today, let’s explorespatial data science. How is it different from spatial analysis? And what are some examples of data science? What is Data Science? (Without Spatial) Data science is the study of information and its source, collection, organization, processing, and presentation. It is anin...
Regardless of where you source your geospatial data, data quality must always be maintained. Poor data results in models of little or limited use. (The cautionary phrase “Bad data in—bad insights out” proves brutally true.) It seems self-evident that organizations can benefit significantly fro...
gather publically available Open Data to enrich their analysis. We also see more & more premium spatial data streams such asFinancial, Human Mobility(otherwise known as Foot Traffic),Road Traffic, Points of Interest, Weather, Climate & Housingbeing used to create more sophisticated spatial models....
Azure Open Datasets are curated public datasets that you can add to scenario-specific features to machine learning solutions, for more accurate models. Open Datasets are available in the cloud, on Microsoft Azure. They're integrated into Azure Machine Learning and readily available to Azure ...
A couple of common examples of data transformations are: Pivoting or changing the orientation of data Converting date formats Aggregating sales and performance data across time Data Preparation for Machine Learning Machine learning is a type of artificial intelligence where algorithms, or models, use ...