<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:contributor>Samantha T. Arundel</dc:contributor>
  <dc:creator>Wenwen Li</dc:creator>
  <dc:date>2022</dc:date>
  <dc:description>&lt;p&gt;&lt;span&gt;This chapter discusses the challenges of traditional spatial analytical methods in their limited capacity to handle big and messy data, as well as mining unknown or latent patterns. It then introduces a new form of spatial analytics—geospatial artificial intelligence (GeoAI)—and describes the advantages of this new strategy in big data analytics and data-driven discovery. Finally, a convergent spatial analytical framework is suggested as a potential future pathway for spatial analysis.&lt;/span&gt;&lt;/p&gt;</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>10.1007/978-981-19-3816-0_17</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>Springer</dc:publisher>
  <dc:title>GeoAI and the future of spatial analytics</dc:title>
  <dc:type>chapter</dc:type>
</oai_dc:dc>