{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/115632"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/115632","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Machine learning models on geographic spatial-temporal data predictions","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","abstract_has_math":false,"creators":["Li, Yanye"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Brunner, Robert J"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05","date_published":"2022-05","updated_at":"2026-07-22T22:24:54Z","subjects":["Machine Learning","Time Series","Spatial-temporal Data"],"languages":["en","eng"],"rights":["Copyright 2022 Yanye Li"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/115632","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Brunner, Robert J"]},{"key":"dc:creator","label":"Author","values":["Li, Yanye"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-05","2022-04-29"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Machine Learning","Time Series","Spatial-temporal Data"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Yanye Li"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/115632"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","The student, Yanye Li, accepted the attached license on 2022-04-28 at 22:57.","The student, Yanye Li, submitted this Thesis for approval on 2022-04-28 at 23:05.","This Thesis was approved for publication on 2022-04-29 at 08:12.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18010 on 2022-11-11 at 12:12:22","Geographic data was not a primary area for early machine learning research. But just as computers rapidly became important tools in radiology, financial trading, and other fields that require fast, highly accurate prediction-based work, machine learning is also showing its ability to push the limits in geospatial data prediction in a very short period. Furthermore, many geographic data analysis include the time dimension to accommodate the temporal dependencies of observations since they often desire to quantify certain changes in environments or landscapes. This added dimension often makes machine learning predictions much harder. In general, it is common for scientists to migrate models used in speech processing, such as recurrent neural networks, to geographic spatial-temporal datasets because the knowledge about temporal dependencies can relatively easily be applied in a similar manner. This thesis first introduces simple regression models and discusses the special considerations required for the three-dimensional data, and this thesis also introduces a state-of-the-art deep learning method, spatial-temporal neural network (STNN), together with its variations. STNN is a specialized recurrent neural network that aims to learn from a series of observations that share both spatial and temporal interactions. We implement these models and compare their performances on experimental results from two different geographic spatial-temporal datasets. Both of the datasets are representative of predictions works in geographic information science, although they differ in some characteristics such as size, timescale, and reversibility. In the end, the comparison leads to a discussion on different strategies of learning and potential improvement."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Machine learning models on geographic spatial-temporal data predictions"]}]}],"canonical_facts":{"dc:contributor":["Brunner, Robert J"],"dc:creator":["Li, Yanye"],"dc:date":["2022-05","2022-04-29"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","The student, Yanye Li, accepted the attached license on 2022-04-28 at 22:57.","The student, Yanye Li, submitted this Thesis for approval on 2022-04-28 at 23:05.","This Thesis was approved for publication on 2022-04-29 at 08:12.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18010 on 2022-11-11 at 12:12:22","Geographic data was not a primary area for early machine learning research. But just as computers rapidly became important tools in radiology, financial trading, and other fields that require fast, highly accurate prediction-based work, machine learning is also showing its ability to push the limits in geospatial data prediction in a very short period. Furthermore, many geographic data analysis include the time dimension to accommodate the temporal dependencies of observations since they often desire to quantify certain changes in environments or landscapes. This added dimension often makes machine learning predictions much harder. In general, it is common for scientists to migrate models used in speech processing, such as recurrent neural networks, to geographic spatial-temporal datasets because the knowledge about temporal dependencies can relatively easily be applied in a similar manner. This thesis first introduces simple regression models and discusses the special considerations required for the three-dimensional data, and this thesis also introduces a state-of-the-art deep learning method, spatial-temporal neural network (STNN), together with its variations. STNN is a specialized recurrent neural network that aims to learn from a series of observations that share both spatial and temporal interactions. We implement these models and compare their performances on experimental results from two different geographic spatial-temporal datasets. Both of the datasets are representative of predictions works in geographic information science, although they differ in some characteristics such as size, timescale, and reversibility. In the end, the comparison leads to a discussion on different strategies of learning and potential improvement."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/115632"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Yanye Li"],"dc:subject":["Machine Learning","Time Series","Spatial-temporal Data"],"dc:title":["Machine learning models on geographic spatial-temporal data predictions"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:54Z"}