Location prediction using forward geocoding for fire incident
Urban fires, although not a natural disaster, are a severe threat that often occurs in urban areas. Banjarmasin City, the capital of South Kalimantan Province and one of the most populous cities in Kalimantan, recorded 159 fire cases between 2020 and 2022, averaging nearly 53 cases yearly. In today’...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/09/e3sconf_issat2024_07031.pdf |
| _version_ | 1797343462084837376 |
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| author | Irawan Faris Ade Danoedoro Projo Farda Nur Mohammad |
| author_facet | Irawan Faris Ade Danoedoro Projo Farda Nur Mohammad |
| author_sort | Irawan Faris Ade |
| collection | DOAJ |
| description | Urban fires, although not a natural disaster, are a severe threat that often occurs in urban areas. Banjarmasin City, the capital of South Kalimantan Province and one of the most populous cities in Kalimantan, recorded 159 fire cases between 2020 and 2022, averaging nearly 53 cases yearly. In today’s digital era, people often share ongoing fire incidents using smartphones and update information on social media and online news. However, the resulting data could be more structured to serve as a dataset. This research addresses these issues by applying geocoding, a digital service that translates street addresses into geographic coordinates. This research uses three geocoders: Google Maps API, Bing Maps API, and Smart Monkey Geocoder. The accuracy of the three geocoders was tested using the Root Mean Square Error (RMSE) statistical method by comparing the geocoding results with valid locations. Prediction analysis was used to identify the next fire event through the density approach of the previous fire event points. This research is expected to provide insights into efficient data collection and structured data conversion, recommendations regarding the best geocoding service, and prediction of fire vulnerability locations based on recurring factors of fire incidents in the area. In conclusion, accurate data is the key to effective fire prediction. |
| first_indexed | 2024-03-08T10:48:03Z |
| format | Article |
| id | doaj.art-2ca47aea766f4590b68391138a7d6c0f |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-08T10:48:03Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-2ca47aea766f4590b68391138a7d6c0f2024-01-26T16:52:43ZengEDP SciencesE3S Web of Conferences2267-12422024-01-014790703110.1051/e3sconf/202447907031e3sconf_issat2024_07031Location prediction using forward geocoding for fire incidentIrawan Faris Ade0Danoedoro Projo1Farda Nur Mohammad2Doctoral Student of the Faculty of Geography, Universitas Gadjah MadaDepartment of Geographic Information Science, Faculty of Geography, Universitas Gadjah MadaDepartment of Geographic Information Science, Faculty of Geography, Universitas Gadjah MadaUrban fires, although not a natural disaster, are a severe threat that often occurs in urban areas. Banjarmasin City, the capital of South Kalimantan Province and one of the most populous cities in Kalimantan, recorded 159 fire cases between 2020 and 2022, averaging nearly 53 cases yearly. In today’s digital era, people often share ongoing fire incidents using smartphones and update information on social media and online news. However, the resulting data could be more structured to serve as a dataset. This research addresses these issues by applying geocoding, a digital service that translates street addresses into geographic coordinates. This research uses three geocoders: Google Maps API, Bing Maps API, and Smart Monkey Geocoder. The accuracy of the three geocoders was tested using the Root Mean Square Error (RMSE) statistical method by comparing the geocoding results with valid locations. Prediction analysis was used to identify the next fire event through the density approach of the previous fire event points. This research is expected to provide insights into efficient data collection and structured data conversion, recommendations regarding the best geocoding service, and prediction of fire vulnerability locations based on recurring factors of fire incidents in the area. In conclusion, accurate data is the key to effective fire prediction.https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/09/e3sconf_issat2024_07031.pdf |
| spellingShingle | Irawan Faris Ade Danoedoro Projo Farda Nur Mohammad Location prediction using forward geocoding for fire incident E3S Web of Conferences |
| title | Location prediction using forward geocoding for fire incident |
| title_full | Location prediction using forward geocoding for fire incident |
| title_fullStr | Location prediction using forward geocoding for fire incident |
| title_full_unstemmed | Location prediction using forward geocoding for fire incident |
| title_short | Location prediction using forward geocoding for fire incident |
| title_sort | location prediction using forward geocoding for fire incident |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/09/e3sconf_issat2024_07031.pdf |
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