공간 빅데이터를 활용한 도시 활력 분석 : 대구시를 사례로 = Urban Vitality Analysis Using Spatial Big Data : A Case Study of Daegu

다국어 초록 (Multilingual Abstract)

In this study, the urban vitality in Daegu, Korea was analyzed at various temporal and spatial scales using spatial big data reflecting the activities of urban residents. Previous urban vitality studies were biased toward the urban physical structure and visual landscape from architectural and landscape perspectives even though they focused on the activities of the urban residents. With the recent development of information and communications technologies(ICT), this study measured urban vitality in Daegu, Korea using spatial big data to explain the activities of urban residents more directly. To do so, five urban vitality indicators were used. The first addresses social factors through SKTelecom-based service population. The second explores traffic flow through public transportation smart card data. The third focuses on economic factors via Hyundai credit card-based data. The fourth looks at human-environment factors through Visible Infrared Imaging Radiometer Suite-Day and Night Band(VIIR-DNB) night lighting intensity data while the final indicator uses public Wi-Fi Access Point. Using principal components analysis(PCA) which summarizes these five indicators efficiently, the urban vitality index was derived for two spatial units(administrative and census output area) and four temporal units(activity hours, rush hour, weekdays and weekend).
The results from this study are summarized as follows. The spatial distribution of the five city vital indicators all appeared high around Jung-gu, the center of Daegu. Social factors were particularly high in the Dongdaegu station area, Daegu bank intersection, Beomeo intersection area and Chilgok 3 district along with the city center. Traffic flow factors were also highest in the center of the city and were relatively high in Dongdaegu station area, Beomeo intersection, Duryu intersection and Seongseo industrial complex. Unlike social factors, they were relatively high on the outskirts of the city. Economic and human-environmental factors were also high in the downtown and the Dongdaegu station area and the virtual factors were relatively high in the Seo-gu along with the downtown area.
Based on these indicators, the results from measuring the urban vitality using PCA are as follows. The Central Business District(CBD) in Daegu showed very high urban vitality near Daegu station, Jungang-ro, Banwoldang intersection, and Dongseong-ro areas of Jung-gu as well as the Seomun market located in the west and the Kyungpook National University Hospital station and Bangcheon market located in the east. In addition, Daegu bank intersection and Beomeo intersection in Suseong-gu, and the area close to the center of the city showed high vitality as did Daegu opera house and Chimsan intersection in Buk-gu.
By region, Suseong-gu additionally showed high vitality around Manchon intersection, Shiji, and Daegu stadium, while in Dong-gu, Shinsegae department store in Dongdaegu complex transfer center and the area in front of it were very high. Also, MBC intersection near Beomeo intersection and Yulha area were high. In Nam-gu, there was high vitality from Yeungnam University hospital to Yeungnam University College and Apsan intersection. In Dalseo-gu, results showed high vitality in Bonri and Jukjeon intersection, Lotte department store(Sangin), Wolseong intersection and Keimyung University east gate area. In Buk-gu, the urban vitality was the highest in Chilgok 3 district and was high in the Northern intercity bus terminal, Kyungpook National University north gate, Bokhyun five-way intersection and the EXCO area. Finally, Dalseong-gun showed low vitality overall, but relatively high vitality in Daegu Technopolis.
The results of the differences in various temporal and spatial scales are as follows. Overall, urban vitality shows similar spatial distribution in both administrative and census output areas. However, in the census output area of a smaller spatial scale, the urban vitality could be measured for more specific areas. In the four temporal scales, the spatial distribution of urban vitality was similar in general, but there were some differences. For example, Lotte department store(Sangin) and the Seongseo campus of Keimyung University and Chilgok 3 districts showed higher urban vitality in activity time than rush hour. In addition, Daegu innovation city showed especially high urban vitality on weekends compared to weekdays as did Daemyung-dong, Bisan-dong where intercity bus terminals are located and Dasa-eup residential areas.
The significance of this study is as follows. First, this study measured urban vitality using spatial big data reflecting the activities of urban residents, unlike the existing urban vitality studies which focus on physical and landscape factors. Based on this, the study will be able to be used as basic data for sustainable urban planning in Daegu. Second, this study analyzed more aspects of urban vitality than other studies explained as a single variable of the floating population. In addition, by considering various temporal scales rather than specific points of time, the characteristics of dynamic modern urban space can be considered better. Finally, based on the high explanatory power of the traffic factors and human-environment factors(respectively) shown in the results, the study will be an important type of basic data for public transportation service in Daegu as well as for analysis of nighttime urban vitality in the future.

목차 (Table of Contents)

• 목차
• Ⅰ. 서론 1
• 1. 연구배경 및 목적 1
• 2. 연구범위 및 방법 3
• Ⅱ. 이론적 배경 및 선행연구의 검토 11
• 1. 도시 활력의 개념 및 측정 11
• 2. 공간 빅데이터의 개념 및 활용 15
• 1) 공간 빅데이터의 개념 15
• 2) 공간 빅데이터의 특성 17
• 3) 공간 빅데이터의 활용 19
• 3. 도시 활력 분석을 위한 공간 빅데이터의 활용 가능성 20
• 4. 선행연구의 검토 21
• 1) 도시 활력에 관한 연구 22
• 2) 도시 역동성 및 도시 활동에 관한 연구 25
• 3) 가로 활력에 관한 연구 27
• 4) 야간 위성영상 데이터를 활용한 도시 공간 분석에 관한 연구 29
• 5) 빅데이터를 활용한 도시 공간 분석에 관한 연구 31
• Ⅲ. 대구시 공간 빅데이터의 분포 특성 35
• 1. 서비스인구 35
• 2. 대중교통 44
• 3. 카드매출 60
• 4. 야간 조명 세기 68
• 5. 공공 와이파이 70
• Ⅳ. 대구 도시 활력의 분석 73
• 1. 도시 활력의 측정 73
• 1) 활동시간 73
• 2) 출·퇴근시간 84
• 3) 주중 93
• 4) 주말 102
• 2. 결과에 대한 고찰 111
• Ⅴ. 요약 및 결론 124
• 1. 요약 124
• 2. 정책적 제언 132
• 참고문헌 134
• Abstract 141