# Regional Traffic Event Detection Using Data Crowdsourcing
## 3 Proposed Regional Traffic Event Detection Technique
### Region Extraction from Event Occurrence
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification** & **Subclassification** & **Definition** \\ \hline LOCATION (LC) & LC\_OTERS & Other places that are not specific LC-series types \\  & LCP\_COUNTRY & Country name \\  & LCP\_PROVINCE & Name of region such as province or state \\  & LCP\_COUNTY & Name of Korean administrative subdistrict, \\  & LCP\_CITY & e.g., Gun, Myeon, Eup, Ri, Dong \\  & LCP\_CITY & City name \\  & LCP\_CAPITALCITY & Capital city name \\  & LCG\_RIVER & River, lake, pond \\  & LCG\_OCEAN & Ocean, sea \\  & LCG\_BAY & Peninsula, bay \\  & LCG\_MOUNTAIN & Mountain, mountain range, ridge, pass/hill, \\  & LCG\_ISLAND & Island, archipelago \\  & LCG\_CONTINENT & Continent \\  & LC\_TOUR & Tourist attractions \\  & LC\_SPACE & Celestial body name \\  & OG\_OTHERS & Other organizations/associations \\  & OGG\_ECONOMY & Economic organization/association, \\  & OGG\_EDUCATION & \begin{tabular}{c} Educational organization/association, \\ education-related organization \\ \end{tabular} \\  & OGG\_MILTARY & Military organization/association and type, \\ ORGANIZATION & & national defense organization \\ (OG) & OGG\_MEDIA & Media organization/association, \\  & OGG\_SPORTS & Sports organization/association \\  & OGG\_ART & Art organization/association \\  & OGG\_MEDICINE & Medical/health organization/association \\  & OGG\_RELIGION & Religious organization/association, \\  & OGG\_SCIENCE & \
\begin{tabular}{c} including sets \\ Scientific organization/association \\ Library or library-related \\ organization/association \\ Legal organization/association \\ Government/administrative organization, \\ public organization, political organization \\ Food-related business/company \\ \end{tabular} \\  & OGG\_HOTEL & Lodging-related business \\ ARTIFACTS (AF) & AF\_BUILDING &
\begin{tabular}{c} Building/civil engineering structure, \\ playground name, apartment, bridge, \\ lighthouse, fountain \\ Road/railway name \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Entity name tag definitions. analyzed and mapped to a geographic information database. For geocoding, an API inputs addresses or location information as text strings and converts them into geographic coordinates. As a result, the user can obtain geographic information through simple API calls. Typical geocoding services include Google Maps API and Naver Maps API. Since the data provided by APIs differ, accuracy is increased using several APIs rather than a single API. This measure is important because, when geocoding, APIs are used to extract administrative districts from location keywords, so some administrative districts may not be recognized. Additionally, because the last keyword is the keyword closest to the event, geocoding is performed based on the last extracted administrative district, among others. Algorithm 1 shows the event occurrence region extraction algorithm. The subclassification items of LOCATION(LC) and ORGANIZATION(OG), tags representing regions among the entity name tag set, and AF_ROAD and AF_BUILDING, subclassifications of ARTIFACTS(AF), are defined as the new array all_Local. To perform keyword extraction in each post, an entity name recognition API obtains entity name tags for each word. If the input entity name tag exists in all_Local, it is considered a keyword representing a region name and is stored in the keyword array. Administrative district extraction is performed based on the selected keywords. A geocoding API is used to convert the administrative districts, and the converted administrative districts are divided and stored as metropolitan local governments, basic local governments, and subdistricts.