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    <dcat:Catalog>
        <dcat:dataset>
            <dcat:Dataset>
                <dct:description xml:lang="kr">일상생활 주변에서 접하는 안전 위험요소를 국민들이 간편하게 신고하고 처리결과를 확인할 수 있도록 행정안전부에서 구축한 "안전신문고"를 이용한 불법주정차 신고에 대한 경기도 파주시의 과태료 처분 현황에 대한 데이터로, 행정구역 정보인 시도명과 시군구명, 신고일시, 위반 장소의 위치 식별 정보인 읍면동명, 도로명주소 및 지번주소, 위도·경도 좌표, 행정처분의 진행 상황과 위반 특성을 파악할 수 있는 과태료부과여부와 위반유형 등으로 구성되어 있습니다. 이 데이터셋은 AI 기반 불법주정차 예측 모델 개발, 스마트시티 교통운영 정책 수립, 시민 참여 기반 안전문화 연구, 위험지역 자동알림·내비게이션 서비스 고도화, 도시 공간 빅데이터 기반 상권·생활권 분석, 공공정책 성과 평가 및 행정 효율화 연구, 도시교통정책 연구, 주정차 위반 패턴 분석, 안전관리 시스템 고도화 등에 활용될 수 있습니다.</dct:description>
                <dct:description xml:lang="en">This data is about the status of fines imposed in Paju-si, Gyeonggi-do, for illegal parking reports made using the \Safety Report Box\ established by the Ministry of the Interior and Safety to allow citizens to easily report safety hazards encountered in their daily lives and check the results. It consists of administrative district information such as city and county names, report date and time, location identification information of the violation location such as town/village/dong name, road name and lot number address, latitude/longitude coordinates, and whether a fine has been imposed and the type of violation to understand the progress of administrative disposition and the characteristics of the violation. This dataset can be utilized for the development of an AI-based illegal parking prediction model, the establishment of smart city traffic operation policies, research on safety culture based on citizen participation, advancement of automatic notification and navigation services for dangerous areas, analysis of commercial and living areas based on urban spatial big data, public policy performance evaluation and administrative efficiency research, urban transportation policy research, analysis of parking violation patterns, advancement of safety management systems, etc.</dct:description>
                <dct:title xml:lang="kr">경기도 파주시_안전신문고불법주정차과태료부과현황</dct:title>
                <dct:title xml:lang="en">Paju-si, Gyeonggi-do_Status of fines for illegal parking at the Safety Newspaper Library</dct:title>
                <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2025-11-17</dct:issued>
                <dct:modified rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2025-11-17</dct:modified>
                <dct:publisher>
                    <foaf:Organization>
                        <foaf:name>경기도 파주시</foaf:name>
                        
                    </foaf:Organization>
                </dct:publisher>
                <dcat:contactPoint>
                    <vcard:Organization>
                        <vcard:organization-unit>정보통신과</vcard:organization-unit>
                        <vcard:hasTelephone rdf:resource="031-940-4226"/>
                    </vcard:Organization>
                </dcat:contactPoint>
                <dct:conformsTo ></dct:conformsTo>
                <dcat:theme>교통및물류 - 도로</dcat:theme>
                <dcat:keyword xml:lang="kr">보행안전,심야불법주정차,통행방해,주차인프라,현장신고,교통약자보호,친환경교통</dcat:keyword>
                <dcat:keyword xml:lang="en">Pedestrian safety,Illegal parking at night,obstruction of traffic,parking infrastructure,On-site report,Protection of the vulnerable in transportation,eco-friendly transportation</dcat:keyword>
                <dct:rights>이용허락범위 제한 없음</dct:rights>
                <dct:accrualPeriodicity>연간</dct:accrualPeriodicity>
                
                <dcat:landingPage rdf:resource="https://www.data.go.kr/data/15152523/fileData.do"/>
                <dcat:distribution>
                    <dcat:Distribution>
                        <dct:accessURL ></dct:accessURL>
                        <dcat:description></dcat:description>
                        <dcat:format>csv</dcat:format>
                        <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2025-11-17</dct:issued>
                        <dcat:title>경기도 파주시_안전신문고불법주정차과태료부과현황_20251117</dcat:title>
                    </dcat:Distribution>
                </dcat:distribution>
                <dct:spatial></dct:spatial>
                <dct:temporal>2024.1.1. ~ 2024.12.31.</dct:temporal>
            </dcat:Dataset>
        </dcat:dataset>

        
    </dcat:Catalog>
</rdf:RDF>