{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T21:27:26Z","timestamp":1768253246690,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":42,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T00:00:00Z","timestamp":1635811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["IIS-1849971, SES-1831615, CNS-2031418, CCF-2046816"],"award-info":[{"award-number":["IIS-1849971, SES-1831615, CNS-2031418, CCF-2046816"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000183","name":"Army Research Office","doi-asserted-by":"publisher","award":["W911NF-21-1-0312"],"award-info":[{"award-number":["W911NF-21-1-0312"]}],"id":[{"id":"10.13039\/100000183","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,11,2]]},"DOI":"10.1145\/3474717.3484260","type":"proceedings-article","created":{"date-parts":[[2021,11,4]],"date-time":"2021-11-04T22:59:49Z","timestamp":1636066789000},"page":"564-575","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["A-GWR"],"prefix":"10.1145","author":[{"given":"Mohammad Reza","family":"Shahneh","sequence":"first","affiliation":[{"name":"University of California, Riverside, Riverside, USA"}]},{"given":"Samet","family":"Oymak","sequence":"additional","affiliation":[{"name":"University of California, Riverside, Riverside, USA"}]},{"given":"Amr","family":"Magdy","sequence":"additional","affiliation":[{"name":"University of California, Riverside, Riverside, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,11,4]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"[n.d.]. https:\/\/github.com\/mshahneh\/AGWR  [n.d.]. https:\/\/github.com\/mshahneh\/AGWR"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Stuart Brown Vincent Versace L. Laurenson Jonathon Fawcett and Scott Salzman. 2012. Assessment of Spatiotemporal Varying Relationships Between Rainfall Land Cover and Surface Water Area Using Geographically Weighted Regression. Environmental Modeling & Assessment - ENVIRON MODEL ASSESS 17 (06 2012).  Stuart Brown Vincent Versace L. Laurenson Jonathon Fawcett and Scott Salzman. 2012. Assessment of Spatiotemporal Varying Relationships Between Rainfall Land Cover and Surface Water Area Using Geographically Weighted Regression. Environmental Modeling & Assessment - ENVIRON MODEL ASSESS 17 (06 2012).","DOI":"10.1007\/s10666-011-9289-8"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apgeog.2012.01.005"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_1_5_1","volume-title":"Jarvis","author":"Crosby Henry","year":"2016","unstructured":"Henry Crosby , Paul Davis , Theodoros Damoulas , and Stephen A . Jarvis . 2016 . A Spatio-temporal, Gaussian Process Regression, Real-estate Price Predictor. In ACM SIGSPATIAL. Henry Crosby, Paul Davis, Theodoros Damoulas, and Stephen A. Jarvis. 2016. A Spatio-temporal, Gaussian Process Regression, Real-estate Price Predictor. In ACM SIGSPATIAL."},{"key":"e_1_3_2_1_6_1","unstructured":"Dgomonov. 2019. New York City Airbnb Open Data. https:\/\/www.kaggle.com\/dgomonov\/new-york-city-airbnb-open-data\/metadata  Dgomonov. 2019. New York City Airbnb Open Data. https:\/\/www.kaggle.com\/dgomonov\/new-york-city-airbnb-open-data\/metadata"},{"key":"e_1_3_2_1_7_1","volume-title":"Weighted Least-Squares Method","author":"Dodge Yadolah","unstructured":"Yadolah Dodge . 2008. Weighted Least-Squares Method . Springer New York , 566--569. Yadolah Dodge. 2008. Weighted Least-Squares Method. Springer New York, 566--569."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1080\/13658816.2019.1707834"},{"key":"e_1_3_2_1_9_1","volume-title":"Oshan et. al","author":"Taylor","year":"2021","unstructured":"Taylor M. Oshan et. al . 2021 . PySAL Datasets . https:\/\/sgsup.asu.edu\/sparc\/multiscale-gwr Taylor M. Oshan et. al. 2021. PySAL Datasets. https:\/\/sgsup.asu.edu\/sparc\/multiscale-gwr"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtrangeo.2018.03.002"},{"key":"e_1_3_2_1_11_1","volume-title":"Geographically Weighted Regression: The Analysis of Spatially Varying Relationships","author":"Fotheringham Alexander","year":"2002","unstructured":"Alexander Fotheringham , Chris Brunsdon , and Martin Charlton . 2002. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships . John Wiley & Sons 13 (01 2002 ). Alexander Fotheringham, Chris Brunsdon, and Martin Charlton. 2002. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. John Wiley & Sons 13 (01 2002)."},{"key":"e_1_3_2_1_12_1","volume-title":"Geographical and Temporal Weighted Regression (GTWR). Geographical Analysis 47 (04","author":"Fotheringham A.","year":"2015","unstructured":"A. Fotheringham , Ricardo Crespo , and Jing Yao . 2015. Geographical and Temporal Weighted Regression (GTWR). Geographical Analysis 47 (04 2015 ). A. Fotheringham, Ricardo Crespo, and Jing Yao. 2015. Geographical and Temporal Weighted Regression (GTWR). Geographical Analysis 47 (04 2015)."},{"key":"e_1_3_2_1_13_1","volume-title":"Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers 107 (08","author":"Fotheringham Alexander","year":"2017","unstructured":"Alexander Fotheringham , Wenbai Yang , and Wei Kang . 2017. Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers 107 (08 2017 ), 1247--1265. Alexander Fotheringham, Wenbai Yang, and Wei Kang. 2017. Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers 107 (08 2017), 1247--1265."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3151547.3151553"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1080\/13658816.2021.1871618"},{"key":"e_1_3_2_1_16_1","unstructured":"harlfoxem. 2016. House Sales in King County USA. https:\/\/www.kaggle.com\/harlfoxem\/housesalesprediction\/metadata  harlfoxem. 2016. House Sales in King County USA. https:\/\/www.kaggle.com\/harlfoxem\/housesalesprediction\/metadata"},{"key":"e_1_3_2_1_17_1","volume-title":"Grid-Enabling Geographically Weighted Regression: A Case Study of Participation in Higher Education in England. T. GIS 14 (02","author":"Harris Richard","year":"2010","unstructured":"Richard Harris , Alexander Singleton , Daniel Grose , Chris Brunsdon , and Paul Longley . 2010. Grid-Enabling Geographically Weighted Regression: A Case Study of Participation in Higher Education in England. T. GIS 14 (02 2010 ), 43--61. Richard Harris, Alexander Singleton, Daniel Grose, Chris Brunsdon, and Paul Longley. 2010. Grid-Enabling Geographically Weighted Regression: A Case Study of Participation in Higher Education in England. T. GIS 14 (02 2010), 43--61."},{"key":"e_1_3_2_1_18_1","volume-title":"The Eighth International Conference on Advanced Geographic Information Systems, Applications, and Services, Venice, GEOProcessing","author":"Irfan M.","year":"2016","unstructured":"M. Irfan , A. Koj , H. Thomas , and M. Sedighi . 2016. Geographical General Regression Neural Network (GGRNN) tool for geographically weighted regression analysis . In The Eighth International Conference on Advanced Geographic Information Systems, Applications, and Services, Venice, GEOProcessing 2016 . M. Irfan, A. Koj, H. Thomas, and M. Sedighi. 2016. Geographical General Regression Neural Network (GGRNN) tool for geographically weighted regression analysis. In The Eighth International Conference on Advanced Geographic Information Systems, Applications, and Services, Venice, GEOProcessing 2016."},{"key":"e_1_3_2_1_19_1","volume-title":"Jamieson and Ameet Talwalkar","author":"Kevin","year":"2015","unstructured":"Kevin G. Jamieson and Ameet Talwalkar . 2015 . Non-stochastic Best Arm Identification and Hyperparameter Optimization. CoRR abs\/1502.07943 (2015). Kevin G. Jamieson and Ameet Talwalkar. 2015. Non-stochastic Best Arm Identification and Hyperparameter Optimization. CoRR abs\/1502.07943 (2015)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12942-017-0085-9"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12942-017-0085-9"},{"key":"e_1_3_2_1_22_1","volume-title":"Geographically Weighted Machine Learning and Downscaling for High-Resolution Spatiotemporal Estimations of Wind Speed. Remote Sensing 11, 11","author":"Lianfa Li.","year":"2019","unstructured":"Lianfa Li. 2019. Geographically Weighted Machine Learning and Downscaling for High-Resolution Spatiotemporal Estimations of Wind Speed. Remote Sensing 11, 11 ( 2019 ). Lianfa Li. 2019. Geographically Weighted Machine Learning and Downscaling for High-Resolution Spatiotemporal Estimations of Wind Speed. Remote Sensing 11, 11 (2019)."},{"key":"e_1_3_2_1_23_1","volume-title":"Efficient Hyperparameter Optimization and Infinitely Many Armed Bandits. CoRR abs\/1603.06560","author":"Li Lisha","year":"2016","unstructured":"Lisha Li , Kevin G. Jamieson , Giulia DeSalvo , Afshin Rostamizadeh , and Ameet Talwalkar . 2016. Efficient Hyperparameter Optimization and Infinitely Many Armed Bandits. CoRR abs\/1603.06560 ( 2016 ). Lisha Li, Kevin G. Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar. 2016. Efficient Hyperparameter Optimization and Infinitely Many Armed Bandits. CoRR abs\/1603.06560 (2016)."},{"key":"e_1_3_2_1_24_1","volume-title":"International Journal of Geographical Information Science (10","author":"Li Ziqi","year":"2018","unstructured":"Ziqi Li , Alexander Fotheringham , Wenwen Li , and Taylor Oshan . 2018. Fast Geographically Weighted Regression (FastGWR): A Scalable Algorithm to Investigate Spatial Process Heterogeneity in Millions of Observations . International Journal of Geographical Information Science (10 2018 ). Ziqi Li, Alexander Fotheringham, Wenwen Li, and Taylor Oshan. 2018. Fast Geographically Weighted Regression (FastGWR): A Scalable Algorithm to Investigate Spatial Process Heterogeneity in Millions of Observations. International Journal of Geographical Information Science (10 2018)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1080\/13658816.2020.1720692"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1080\/13658816.2016.1263731"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2021.102784"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12936-015-0976-9"},{"key":"e_1_3_2_1_29_1","unstructured":"Fernando Nogueira. 2014-. Bayesian Optimization: Open source constrained global optimization tool for Python. https:\/\/github.com\/fmfn\/BayesianOptimization  Fernando Nogueira. 2014-. Bayesian Optimization: Open source constrained global optimization tool for Python. https:\/\/github.com\/fmfn\/BayesianOptimization"},{"key":"e_1_3_2_1_30_1","volume-title":"Levi John Wolf, and Alexander Stewart Fotheringham","author":"Oshan Taylor M.","year":"2018","unstructured":"Taylor M. Oshan , Ziqi Li , Wei Kang , Levi John Wolf, and Alexander Stewart Fotheringham . 2018 . mgwr: A Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. OSF Preprints bphw9. Center for Open Science . Taylor M. Oshan, Ziqi Li, Wei Kang, Levi John Wolf, and Alexander Stewart Fotheringham. 2018. mgwr: A Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. OSF Preprints bphw9. Center for Open Science."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi8060269"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.52324\/001c.8285"},{"key":"e_1_3_2_1_34_1","volume-title":"Mokbel","author":"Sabek Ibrahim","year":"2018","unstructured":"Ibrahim Sabek , Mashaal Musleh , and Mohamed F . Mokbel . 2018 . TurboReg: a Framework for Scaling Up Spatial Logistic Regression Models. In SIGSPATIAL. Ibrahim Sabek, Mashaal Musleh, and Mohamed F. Mokbel. 2018. TurboReg: a Framework for Scaling Up Spatial Logistic Regression Models. In SIGSPATIAL."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"crossref","unstructured":"Masamichi Shimosaka Takeshi Tsukiji Hideyuki Wada and Kota Tsubouchi. 2018. Predictive Population Behavior Analysis from Multiple Contexts with Multilinear Poisson Regression. In ACM SIGSPATIAL.  Masamichi Shimosaka Takeshi Tsukiji Hideyuki Wada and Kota Tsubouchi. 2018. Predictive Population Behavior Analysis from Multiple Contexts with Multilinear Poisson Regression. In ACM SIGSPATIAL.","DOI":"10.1145\/3274895.3274964"},{"key":"e_1_3_2_1_36_1","volume-title":"Proceedings of the 25th International Conference on Neural Information Processing Systems -","volume":"2","author":"Snoek Jasper","unstructured":"Jasper Snoek , Hugo Larochelle , and Ryan P. Adams . 2012. Practical Bayesian Optimization of Machine Learning Algorithms . In Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 2 (Lake Tahoe, Nevada) (NIPS'12). Curran Associates Inc., Red Hook, NY, USA, 2951--2959. Jasper Snoek, Hugo Larochelle, and Ryan P. Adams. 2012. Practical Bayesian Optimization of Machine Learning Algorithms. In Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 2 (Lake Tahoe, Nevada) (NIPS'12). Curran Associates Inc., Red Hook, NY, USA, 2951--2959."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/9.119632"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.2307\/143141"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.landurbplan.2012.03.010"},{"key":"e_1_3_2_1_40_1","volume-title":"An Extension of Geographically Weighted Regression with Flexible Bandwidths. In the 20th Annual GIS Research UK (GISRUK) conference.","author":"Yang Wenbai","year":"2012","unstructured":"Wenbai Yang , Alexander Fotheringham , and Paul Harris . 2012 . An Extension of Geographically Weighted Regression with Flexible Bandwidths. In the 20th Annual GIS Research UK (GISRUK) conference. Wenbai Yang, Alexander Fotheringham, and Paul Harris. 2012. An Extension of Geographically Weighted Regression with Flexible Bandwidths. In the 20th Annual GIS Research UK (GISRUK) conference."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1631\/FITEE.1700808"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2016.2520480"}],"event":{"name":"SIGSPATIAL '21: 29th International Conference on Advances in Geographic Information Systems","location":"Beijing China","acronym":"SIGSPATIAL '21","sponsor":["SIGSPATIAL ACM Special Interest Group on Spatial Information"]},"container-title":["Proceedings of the 29th International Conference on Advances in Geographic Information Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3474717.3484260","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3474717.3484260","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3474717.3484260","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:11:46Z","timestamp":1750191106000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3474717.3484260"}},"subtitle":["Fast and Accurate Geospatial Inference via Augmented Geographically Weighted Regression"],"short-title":[],"issued":{"date-parts":[[2021,11,2]]},"references-count":42,"alternative-id":["10.1145\/3474717.3484260","10.1145\/3474717"],"URL":"https:\/\/doi.org\/10.1145\/3474717.3484260","relation":{},"subject":[],"published":{"date-parts":[[2021,11,2]]},"assertion":[{"value":"2021-11-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}