Spatial prediction of sparse events using a discrete global grid system; a case study of hate crimes in the USA
Spatial prediction of any geographic phenomenon can be an intractable problem. Predicting sparse and uncertain spatial events related to many influencing factors necessitates the integration of multiple data sources. We present an innovative approach that combines data in a Discrete Global Grid Syst...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2021-06-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17538947.2021.1886356 |
| _version_ | 1797678487600889856 |
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| author | Michael Jendryke Stephen C. McClure |
| author_facet | Michael Jendryke Stephen C. McClure |
| author_sort | Michael Jendryke |
| collection | DOAJ |
| description | Spatial prediction of any geographic phenomenon can be an intractable problem. Predicting sparse and uncertain spatial events related to many influencing factors necessitates the integration of multiple data sources. We present an innovative approach that combines data in a Discrete Global Grid System (DGGS) and uses machine learning for analysis. A DGGS provides a structured input for multiple types of spatial data, consistent over multiple scales. This data framework facilitates the training of an Artificial Neural Network (ANN) to map and predict a phenomenon. Spatial lag regression models (SLRM) are used to evaluate and rank the outputs of the ANN. In our case study, we predict hate crimes in the USA. Hate crimes get attention from mass media and the scientific community, but data on such events is sparse. We trained the ANN with data ingested in the DGGS based on a 50% sample of hate crimes as identified by the Southern Poverty Law Center (SPLC). Our spatial prediction is up to 78% accurate and verified at the state level against the independent FBI hate crime statistics with a fit of 80%. The derived risk maps are a guide to action for policy makers and law enforcement. |
| first_indexed | 2024-03-11T23:00:28Z |
| format | Article |
| id | doaj.art-5f40223a0fd94c4f9cfd4ac4bd2fe154 |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-03-11T23:00:28Z |
| publishDate | 2021-06-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj.art-5f40223a0fd94c4f9cfd4ac4bd2fe1542023-09-21T14:57:10ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552021-06-0114678980510.1080/17538947.2021.18863561886356Spatial prediction of sparse events using a discrete global grid system; a case study of hate crimes in the USAMichael Jendryke0Stephen C. McClure1Wuhan UniversityWuhan UniversitySpatial prediction of any geographic phenomenon can be an intractable problem. Predicting sparse and uncertain spatial events related to many influencing factors necessitates the integration of multiple data sources. We present an innovative approach that combines data in a Discrete Global Grid System (DGGS) and uses machine learning for analysis. A DGGS provides a structured input for multiple types of spatial data, consistent over multiple scales. This data framework facilitates the training of an Artificial Neural Network (ANN) to map and predict a phenomenon. Spatial lag regression models (SLRM) are used to evaluate and rank the outputs of the ANN. In our case study, we predict hate crimes in the USA. Hate crimes get attention from mass media and the scientific community, but data on such events is sparse. We trained the ANN with data ingested in the DGGS based on a 50% sample of hate crimes as identified by the Southern Poverty Law Center (SPLC). Our spatial prediction is up to 78% accurate and verified at the state level against the independent FBI hate crime statistics with a fit of 80%. The derived risk maps are a guide to action for policy makers and law enforcement.http://dx.doi.org/10.1080/17538947.2021.1886356discrete global grid systemgeospatial data integrationartificial neural networkspatial predictionsparse eventshates crimes |
| spellingShingle | Michael Jendryke Stephen C. McClure Spatial prediction of sparse events using a discrete global grid system; a case study of hate crimes in the USA International Journal of Digital Earth discrete global grid system geospatial data integration artificial neural network spatial prediction sparse events hates crimes |
| title | Spatial prediction of sparse events using a discrete global grid system; a case study of hate crimes in the USA |
| title_full | Spatial prediction of sparse events using a discrete global grid system; a case study of hate crimes in the USA |
| title_fullStr | Spatial prediction of sparse events using a discrete global grid system; a case study of hate crimes in the USA |
| title_full_unstemmed | Spatial prediction of sparse events using a discrete global grid system; a case study of hate crimes in the USA |
| title_short | Spatial prediction of sparse events using a discrete global grid system; a case study of hate crimes in the USA |
| title_sort | spatial prediction of sparse events using a discrete global grid system a case study of hate crimes in the usa |
| topic | discrete global grid system geospatial data integration artificial neural network spatial prediction sparse events hates crimes |
| url | http://dx.doi.org/10.1080/17538947.2021.1886356 |
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