Artificial Intelligence (AI) has received tremendous attention from academia, industry, and the general public in recent years. The integration of geography and AI, or GeoAI, provides novel approaches for addressing a variety of problems in the natural environment and our human society. This entry briefly reviews the recent development of AI with a focus on machine learning and deep learning approaches. We discuss the integration of AI with geography and particularly geographic information science, and present a number of GeoAI applications and possible future directions.
As new information and communication technologies have altered so many aspects of our daily lives over the past decades, they have simultaneously stimulated a shift in the types of data that we collect, produce, and analyze. Together, this changing data landscape is often referred to as "big data." Big data is distinguished from "small data" not only by its high volume but also by the velocity, variety, exhaustivity, resolution, relationality, and flexibility of the datasets. This entry discusses the visualization of big spatial datasets. As many such datasets contain geographic attributes or are situated and produced within geographic space, cartography takes on a pivotal role in big data visualization. Visualization of big data is frequently and effectively used to communicate and present information, but it is in making sense of big data – generating new insights and knowledge – that visualization is becoming an indispensable tool, making cartography vital to understanding geographic big data. Although visualization of big data presents several challenges, human experts can use visualization in general, and cartography in particular, aided by interfaces and software designed for this purpose, to effectively explore and analyze big data.
Differentiate the interpretation of a series of three maps and a single multivariate map, each representing the same three related variables
Design a single map symbol that can be used to symbolize a set of related variables
Create a map that displays related variables using different mapping methods (e.g., choropleth
and proportional symbol, choropleth and cartogram) Create a map that displays related variables using the same mapping method (e.g., bivariate choropleth map, bivariate dot map)
Design a map series to show the change in a geographic pattern over time
Detect a multivariate outlier using a combination of maps and graphs
Explain the relationship among several variables in a parallel coordinate plot
This short article introduces the definition of buffer and explains how buffers are created for single or multiple geographic features of different geometric types. It also discusses how buffers are generated differently in vector and raster data models and based on the concept of cost.
This short article introduces the definition of buffer and explains how buffers are created for single or multiple geographic features of different geometric types. It also discusses how buffers are generated differently in vector and raster data models and based on the concept of cost.
List the likely sources of error in slope and aspect maps derived from digital elevation models (DEMs) and state the circumstances under which these can be very severe
Outline how higher order derivatives of height can be interpreted
Explain how slope and aspect can be represented as the vector field given by the first derivative of height
Explain why the properties of spatial continuity are characteristic of spatial surfaces
Explain why zero slopes are indicative of surface specific points such as peaks, pits, and passes, and list the conditions necessary for each
Design an algorithm that calculates slope and aspect from a triangulated irregular network (TIN) model
Outline a number of different methods for calculating slope from a DEM
AM-93 - Artificial Intelligence Approaches
Artificial Intelligence (AI) has received tremendous attention from academia, industry, and the general public in recent years. The integration of geography and AI, or GeoAI, provides novel approaches for addressing a variety of problems in the natural environment and our human society. This entry briefly reviews the recent development of AI with a focus on machine learning and deep learning approaches. We discuss the integration of AI with geography and particularly geographic information science, and present a number of GeoAI applications and possible future directions.