https://jtvandijk.github.io/GPU-Analytics/ Recent content in Home Page on GPU-based analysis for social and geographic applications Hugo -- gohugo.io en-us https://jtvandijk.github.io/GPU-Analytics/docs/case_study/chapter_1/ Mon, 01 Jan 0001 00:00:00 +0000 https://jtvandijk.github.io/GPU-Analytics/docs/case_study/chapter_1/ Address geocoding # Address geocoding, or address matching, is an example of a broad class of data science problems known as data linkage. Data linkage problems involve connecting two datasets together by establishing an association between two records based on one or more common properties. In the case of address geocoding, one would try to assign XY coordinates to a list of address strings derived from, for instance, consumer sources that are not explicitly georeferenced (see, for instance, Lansley et al. https://jtvandijk.github.io/GPU-Analytics/docs/case_study/chapter_2/ Mon, 01 Jan 0001 00:00:00 +0000 https://jtvandijk.github.io/GPU-Analytics/docs/case_study/chapter_2/ GeoAI and Deep Learning # GeoAI, or geospatial artificial intelligence (AI), has become a trending topic and the frontier for spatial analytics in Geography (Li and Hsu, 2022). Although the field of AI has experienced highs and lows in the past decades, it has recently gained tremendous momentum because of breakthrough developments in deep (machine) learning, immense available computing power, and the pressing needs for mining and understanding big data. https://jtvandijk.github.io/GPU-Analytics/docs/case_study/chapter_3/ Mon, 01 Jan 0001 00:00:00 +0000 https://jtvandijk.github.io/GPU-Analytics/docs/case_study/chapter_3/ Geospatial Operation and Analysis # Why are we interested in geospatial data? Geospatial data is a type of data that is associated with a location. This location can be a point, a line, a polygon, or a raster. Geospatial data is becoming more and more important, yet the sheer volume of information that is generated made it difficult to handle. In this chapter, we will be exploring the use of GPU for manipulating large-scale geospatial data, and provide a practical example of how we can predict the presence of Aedes aegypti across the globe. https://jtvandijk.github.io/GPU-Analytics/docs/about/gpu/ Mon, 01 Jan 0001 00:00:00 +0000 https://jtvandijk.github.io/GPU-Analytics/docs/about/gpu/ Introduction # Over the past few decades, geographic and social science research has faced challenges due to a lack of available data, the cost and time needed to conduct surveys, and limitations on computational power for analysis. These issues have been exacerbated by a decline in survey quality as well as an increase in biases in the characteristics of respondents. There was also no guarantee that long-term, time-consuming surveys would continue to be available due to fiscal austerity (Singleton et al. https://jtvandijk.github.io/GPU-Analytics/docs/about/setting_up/ Mon, 01 Jan 0001 00:00:00 +0000 https://jtvandijk.github.io/GPU-Analytics/docs/about/setting_up/ Introduction # Setting up the environment for GPU can be challenging, as every computer has different hardware and software configurations. There are no universal instructions that will work for everyone, but in this chapter, we will discuss how to set up the environment using Google Colab as well as a GPU on a remote server. We will also highlight the steps to verify that the GPU is working properly.