The Case of Food Trucks in Seoul, Korea

학위논문(석사)--서울대학교 대학원 :사회과학대학 지리학과,2019. 8. 이건학.As the mobility of people increases, the market vendors in urban areas also obtain spatio-temporal dynamics. The enhanced mobility yields the behavioral change, for not only the customers but also the market vendors business model. The food truck is one of the representative mobile vendors which show the possibility of the mobility of the suppliers. In particular, the deregulation in 2017 enabled food trucks to move to another site. Also, due to the high rent in Seoul, the food truck is considered as a prospective business model for young people. Although there are new characteristics of mobile vendors, such as the food truck, the business model of the mobile vendor when the existing market was already located did not receive academic attention. This research aimed to optimize the locations and routes of the mobile vendor through spatio-temporal analysis. This research designated three research questions. First, the locating trend of mobile vendors, when the existing competitive market already existed, has different result compared to the traditional mobile vendors strategy. Second, the value of the multi-objective optimization method is verified to improve the mobile vendors spatio-temporal optimal location and route sets. Lastly, the potential for improvement of current mobile vendors strategy through spatio-temporal analysis is examined. This research makes a model that targeted the current situation of Seoul. To reflect the spatio-temporal population dynamics, the de facto population data in Seoul was applied to this study. The objectives of the research were to reduce the dependency of the occasional festivals, maximizing the profit of food trucks, and minimizing the conflict between the existing markets and the food trucks. This research followed three steps to achieve those goals. First, based on the descriptive data analysis, it was verified whether the population dynamics existed on the days, time, and region. By analyzing the locations of restaurants, which are competitors to the food trucks, the relationship between the de facto population and the restaurants distribution was empirically proved. Also, unifying two factors to compare the weight was conducted during the first step. The spatial optimization method was applied to find the spatio-temporal optimal locations for food trucks in the second phase. After selecting the feasible area, the optimal food truck locations were found at each time period. To minimize the conflict between the existing restaurants and the food trucks, this research used the multi-objective optimization and made multiple scenarios depending on the weight factor α. As a result, as the α increases, the more food trucks are gathered into the CBDs in Seoul. In the final step, the food trucks spatio-temporal routes were calculated, the minimal distance set of distance was composed, and the results were visualized. The data mining method, K-Means, was applied to capture the spatio-temporal clusters of mobile food trucks. The minimized distance set presented the optimal spatio-temporal locations and routes of food trucks. The results were presented by the 3D mapping method, due to the complexity of the data. In conclusion, the food trucks showed different optimal locations depending on the α, but, at lunch time, the food trucks tended to gather into the CBDs. The reasons for these spatio-temporal patterns are mostly due to the economic and leisure factors during the weekday and the weekend. On the other hand, the food truck locations need to move at dinner time to follow the residential population in the outskirts of Seoul. The Pareto optimal set of this research showed superior results than the current food trucks location, which means minimizing the conflict and maximizing the capturing of demand. This research used multiple methodologies of GIS and spatial optimization to analyze the mobile vendor's spatio-temporal optimal locations and routes. This research has significance in that it has built the model to deal with two separated factors, location, and traffic, in an integrated method with spatio-temporal analysis.도시 내 인구 역동성이 증가하면서, 상업 활동의 수요 역시 시공간적 역동성을 보인다. 도시 내 수요자의 이동성(mobility)이 증가하면서 공급자 역시 수요자의 공간적 변동을 따라 이동할 필요성이 높아진다. 본 연구에서는 이동성을 갖춘 상업시설을 이동식 상업시설(mobile vendor)로 정의하는데, 푸드트럭이 대표적인 이동식 상업시설이다. 푸드트럭은 수요자의 시공간적 핫스팟이 변함에 따라 공급자도 이동할 수 있다는 특성을 대표적으로 보여준다. 중앙정부는 2017년 푸드트럭 규제를 완화하였고 서울시는 관련 조례를 제정하였다. 이 조례에 의거하여 푸드트럭은 기동성을 활용할 수 있게 되었고, 대표적인 청년 창업의 수단으로 각광받고 있다. 기존의 연구들은 이동식 상업시설의 입지에만 주목하면서 고정식 상업이 기입지한 상태에서의 이동식 상업시설 입지 양상을 연구하지는 않았다. 수요의 역동성이 증가하는 현대 사회에서 이동식 상업시설의 시공간적 입지와 이동에 대한 연구도 부족하였다. 본 연구는 기존 상권이 존재하면서, 수요가 시공간적으로 변동하는 상황에서 푸드트럭의 입지와 이동 경로를 최적화하는 방안에 대해 살펴보았다. 본 연구는 세 가지 연구 질문을 제기하고 이에 대한 해답을 찾았다. 첫 번째로 상업 구조가 이미 형성된 상태에서 이동식 상업시설의 입지와 경로의 최적화 모형은 어떠한지 연구하였다. 두 번째로 다목적 최적화 기법이 이동식 상업시설의 최적 입지와 경로 선정 시나리오 구축에 적용 가능한지도 살펴보았다. 마지막으로 시공간적 최적 입지 탐색과 경로 최적화가 현재의 이동식 상업시설의 운영을 개선할 수 있는지도 탐구하였다. 위와 같은 연구 질문에 답하기 위해, 본 연구는 서울시의 현재 상황과 동일하게 500대의 푸드트럭이 운영되는 모델을 구축하였다. 서울시의 생활인구 자료를 활용하여 모델에서의 수요를 대표하는 인구 역동성을 측정하였다. 위 과정을 통해, 현재 푸드트럭의 일회성 축제 매출 의존성을 낮추고, 일상적 상황에서의 수요 포획을 극대화하면서도 기존 상권과의 마찰을 최소화하는 방안을 도출하였다. 연구 목적 달성을 위해 다음 세 가지 단계를 통해 연구를 진행하였다. 첫 번째로는 기술적 자료 분석을 토대로, 인구 역동성이 요일, 시간, 지역적 스케일에서 각각 존재하는지를 검증하였다. 또한, 푸드트럭과 경쟁 업체들의 입지를 검토하여, 서울시의 평일 생활인구와 음식점의 입지 사이의 상호관계를 실증적으로 규명하였다. 각 단위 구역 내의 생활인구와 음식점 수를 가중치로 변환해서 비교하기 위해, 수치를 정규화하고 수요와 경쟁의 지표로 정의하였다. 두 번째로 다목적 공간 최적화 기법을 토대로, 가중치에 따른 시나리오별 푸드트럭 최적 입지를 탐색하였다. 공간 최적화 기법을 적용하기 위해 푸드트럭의 입지 가능 지역을 선택하고, 각 시간대별 푸드트럭 최적 입지를 선정하였다. 푸드트럭 운영자의 수익 보장을 위한 수요 포획 극대화와 기존 상권과의 갈등 최소화라는 두 가지 목적 하에, 다목적 함수를 설계하고 푸드트럭의 시공간적 다목적 공간 최적화 결과를 각각의 수요 가중치 시나리오에 따라 도출하였다. 그 결과, 푸드트럭의 배후지 내 수요 포획 극대화에 높은 가중치를 부여할수록, 도심지에 푸드트럭이 더 많이 입지함을 확인할 수 있었다. 마지막으로 각 시간대별 이동 푸드트럭을 정의해서, 네트워크 이동 거리를 최소화하는 조합을 도출하였고, 이를 시각화 하였다. 푸드트럭의 이동 경향성을 파악하기 위해 데이터 마이닝 기법인 K-Means 클러스터링을 활용하여 클러스터를 구축했고, 각 시공간별 클러스터의 이동 경로를 도출하였다. 그 후 시공간 클러스터들의 이동 거리 합을 최소화하는 방법을 통해, 푸드트럭의 시간대별 입지와 이동 경로를 계산하였다. 이렇게 도출된 시공간 입지, 경로 분석 결과물을 3D 지도로 재현하였다. 연구 결과, 푸드트럭은 목적식의 가중치에 따라 서로 다른 최적 입지를 보이지만, 점심 시간에는 도심 지역에 입지하는 공통점을 보이고 있다. 이는 주중에는 경제활동인구가, 주말에는 여가활동을 즐기는 인구가 도심에 집중되기 때문으로 해석된다. 반면 푸드트럭이 저녁 시간에는 상주인구의 밀집 지역을 따라 서울시 외곽으로 이동하는 경향이 나타난다. 본 연구 성과를 현재의 푸드트럭 입지와 비교한 결과, 기존 상권과의 마찰을 현재보다 감소시키면서도, 수요를 추가 포획할 수 있는 파레토 균형의 달성이 가능함을 확인할 수 있었다. 본 연구는 GIS와 공간 최적화의 다양한 기법을 활용하여, 이동식 상업시설과 기존 상권이 병존할 때의 입지와 이동 양상을 시공간적으로 분석하였다. 본 연구의 의의는 기존의 입지와 교통이라는 두 가지 주요 요소를 시공간적으로 고려하며 통합적으로 다루는 모델을 구축하였다는 점에 있다.1. Introduction 1 1.1. Study Background 1 1.2. Purpose of Research 7 1.3. Organization of Chapters 9 2. Literature Review 11 2.1. Periodic Market Model 11 2.2. Competitive Location Model 13 2.3. Multi-objective Spatial Optimization 15 2.4. Research on Food Trucks 17 3. Research Methodology 19 3.1. Study Area and Data 19 3.1.1. Research Area 19 3.1.2. Data Description 21 3.2. Service Area Analysis 23 3.2.1. Calculating Profit of Each Food Trucks 23 3.2.2. Analyzing Competition of Existing Restaurants 25 3.2.3. Unifying Two Variables 26 3.3. Multi-objective Optimization and Heuristic Algorithm 28 3.3.1. Multi-objective Spatial Optimization 28 3.3.2. Heuristic Algorithm 30 3.4. Data Mining and Vehicle Routing Problem 34 3.4.1. Clustering the Mobile Food Trucks 34 3.4.2. The Spatio-temporal Vehicle Routing Problem 37 4. Research Analysis and Results 42 4.1. Food Trucks Service Area Analysis 42 4.1.1. Global Trend of the De Facto Population 42 4.1.2. Spatial Heterogeneity of Population Distribution 45 4.1.3. Integrating Indexes and Descriptive Analysis 51 4.2. Applying Multi-objective Location Analysis 54 4.2.1. Filtering Feasible Area 54 4.2.2. Pareto Optimal Set 56 4.2.3. Exploring three multi-objective scenarios 58 4.3. Optimal Routes and Visualization of Food Trucks 63 4.3.1. Conditions of the Mobile Food Trucks 63 4.3.2. K-Means Clustering 66 4.3.3. Optimal Routes for Food Trucks 71 4.4. Summary and Discussion 79 5. Conclusion 82 5.1. Summary of Thesis 82 5.2. Discussion 84 5.3. Future Research Direction 85 Bibliography 87 Abstract in Korean 93Maste

PERSONALIZED POINT OF INTEREST RECOMMENDATIONS WITH PRIVACY-PRESERVING TECHNIQUES

Location-based services (LBS) have become increasingly popular, with millions of people using mobile devices to access information about nearby points of interest (POIs). Personalized POI recommender systems have been developed to assist users in discovering and navigating these POIs. However, these systems typically require large amounts of user data, including location history and preferences, to provide personalized recommendations. The collection and use of such data can pose significant privacy concerns. This dissertation proposes a privacy-preserving approach to POI recommendations that address these privacy concerns. The proposed approach uses clustering, tabular generative adversarial networks, and differential privacy to generate synthetic user data, allowing for personalized recommendations without revealing individual user data. Specifically, the approach clusters users based on their fuzzy locations, generates synthetic user data using a tabular generative adversarial network and perturbs user data with differential privacy before it is used for recommendation. The proposed approaches achieve well-balanced trade-offs between accuracy and privacy preservation and can be applied to different recommender systems. The approach is evaluated through extensive experiments on real-world POI datasets, demonstrating that it is effective in providing personalized recommendations while preserving user privacy. The results show that the proposed approach achieves comparable accuracy to traditional POI recommender systems that do not consider privacy while providing significant privacy guarantees for users. The research\u27s contribution is twofold: it compares different methods for synthesizing user data specifically for POI recommender systems and offers a general privacy-preserving framework for different recommender systems. The proposed approach provides a novel solution to the privacy concerns of POI recommender systems, contributes to the development of more trustworthy and user-friendly LBS applications, and can enhance the trust of users in these systems

Aerial Base Station Deployment for Post-Disaster Public Safety Applications

Earthquakes and floods are constant threats to most of the countries in the world. After such catastrophes, a rapid response is needed, which includes communications not only for first responders but also for local civilians. Even though there are technologies and specialized personnel for rapid deployment, it is common that external factors will hinder the arrival of help while communication requirements are urgently required. Such communication technologies would aid tasks regarding organization and information dissemination from authorities to the civilians and vice-versa. This necessity is due to protocols and applications to allocate the number of emergency resources per location and to locate missing people. In this thesis, we investigate the deployment problem of Mobile Aerial Base Stations (MABS). Our main objective is to ensure periodic wireless communication for geographically spread User Equipment (UE) based on LTE technology. First, we establish a precedent of emergency situations where MABS would be useful. We also provide an introduction to the study and work conducted in this thesis. Second, we provide a literature review of existing solutions was made to determine the advantages and disadvantages of certain technologies regarding the described necessity. Third, we determine how MABS, such as gliders or light tactical balloons that are assumed to be moving at an average speed of 50 km/h, will be deployed. These MABS would visit different cluster centroids determined by an Affinity Propagation Clustering algorithm. Additionally, a combination of graph theory and Genetic Algorithm (GA) is implemented through mutators and fitness functions to obtain best flyable paths through an evolution pool of 100. Additionally, Poisson, Normal, and Uniform distributions are utilized to determine the amount of Base Stations and UEs. Then, for every distribution combination, a set of simulations is conducted to obtain the best flyable paths. Serviced UE performance indicators of algorithm efficiency are analyzed to determine whether the applied algorithm is effective in providing a solution to the presented problem. Finally, in Chapter 5, we conclude our work by supporting that the proposed model would suffice the needs of mobile users given the proposed emergency scenario. Adviser: Yi Qia

THE ROLE OF ICT IN EDUCATION: AN EFFICIENCY ANALYSIS

Nell’ambito dell’educazione, l’utilizzo delle tecnologie dell’informazione e della comunicazione (TCI) si è notevolmente intensificato negli ultimi decenni grazie agli investimenti effettuati. Il concetto di TCI è molto ampio. In questo lavoro di tesi, TCI non si riferisce solo alle infrastrutture fisiche (ad esempio radio, telefono, video, televisione, computer), ma include anche l’uso e l’intensità di utilizzo (ad esempio l’impiego giornaliero, settimanale, ecc.), la qualità e l’ubicazione dell’infrastruttura (ad esempio, a scuola oppure a casa), il motivo del suo utilizzo (ad esempio, per svago o per motivi di studio) e la spesa relativa alle TIC. Questa dissertazione discute il ruolo delle TIC nell’istruzione concentrandosi sull’analisi dell’efficienza. La tesi comprende quattro lavori ripartiti in diversi capitoli. Il Capitolo II propone una sistematica literature review sull’argomento. Il Capitolo III esegue un’analisi transnazionale dell’efficienza dell’istruzione a livello scolastico in sei Paesi del sud-est asiatico, ossia in Brunei Darussalam, in Malesia, in Indonesia, nelle Filippine, a Singapore ed in Tailandia. L’analisi viene effettuata mediate l’approccio della stochastic frontier analysis (SFA) che consente di considerare l'eteroschedasticità. Da questo studio risulta che Singapore è comparativamente il Paese con la migliore performance. Nell’analisi condotta, le variabili TIC, ovvero (1) il rapporto tra computer a scuola e (2) il numero totale di studenti ed il rapporto tra computer connessi a Internet, sono assunte essere determinanti dell’inefficienza ed entrano come input nella funzione di produzione (istruzione). Dall’analisi condotta, emerge che il primo rapporto non influenza in modo significativo gli esiti scolastici mentre il secondo ha un significativo impatto. Come determinanti dell’inefficienza, il primo rapporto influisce sull’inefficienza della scuola in nelle aree di matematica e scienze, mentre il secondo non ha alcuna influenza. Il Capitolo IV utilizza l'approccio DEA (non-parametric data envelopment analysis) del modello di super-efficienza che consente alle scuole efficienti di avere punteggi di efficienza superiori a uno (nell’approccio DEA tradizionale, il punteggio di efficienza è limitato da zero a uno). Per studiare i fattori che potenzialmente influenzano l’efficienza, questo studio include anche una seconda analisi basata sull’approccio bootstrapped quantile regression. I risultati suggeriscono una serie di implicazioni politiche per le scuole del sud-est asiatico, indicando diverse linee d’azione per le scuole sia con livelli di efficienza più alti sia per quelle con efficienza minore. Il Capitolo V estende l'analisi condotta nel Capitolo III sia dal punto di vista metodologico che empirico. L’analisi, basata sull’approccio SFA, non include solo le infrastrutture TCI nel modello, ma aggiunge anche l’uso delle TCI (compreso l’indice del tempo trascorso dagli studenti nell’uso delle TCI a scuola, fuori dalla scuola per scopi di intrattenimento e a casa per compiti scolastici). Ciò viene fatto utilizzando il “modello di frontiera stocastica a quattro componenti” in cui le TCI sono modellate sia come input che come determinanti di inefficienza variabile nel tempo. Inoltre, questo modello viene testato utilizzando un set di dati di 24 Paesi OCSE. I risultati mostrano che tutte e tre le variabili che appartengono all’uso delle TIC influenzano i risultati sul livello di istruzione degli studenti, mentre come determinanti di inefficienza, queste variabili hanno solo un effetto marginale. Questo studio dovrebbe quindi fornire una visione più olistica del ruolo delle TIC nell’efficienza dei processi educativi.In education sector, the application of information and communication technology (ICT) has increased substantially over the last decades as many countries have been investing their resources in ICT for educational purposes. The ICT is a broad concept. In this dissertation, ICT does not only refer to physical infrastructure (e.g., radio, telephone, video, television, computer), but it also includes the use and the intensity of use (e.g., every day, one a week, twice a week), the quality and location of the infrastructure (e.g., at school, at home), the reason for using it (e.g., for entertainment or for study purposes), and the expenditure related to the ICT. This dissertation then discusses the role of ICT in education focusing on the efficiency analysis. It comprises four studies starting with a systematic literature review presented in Chapter II, which offers a clear overview of what has and has not been done in the literature towards this particular topic. Chapter III performs cross-country analysis of efficiency of education at school level in six countries in South-East Asia (i.e., Brunei Darussalam, Malaysia, Indonesia, the Philippines, Singapore, and Thailand). The stochastic frontier analysis (SFA) allowing for heteroscedasticity is used. The result reveals that Singapore has the (relatively) best performance among other countries. The ICT infrastructure variables, i.e., the ratio of computers at school to the total number of students and the ratio of computers connected to the internet, are modeled as inputs in the (education) production function and determinants of inefficiency. The first ratio is found to be not significant influencing education outcomes while the second one does influence. As determinants of inefficiency, the first ratio affects school’s inefficiency in terms of mathematics and science, while the second one has no influence. Relying the finding of Chapter III that there are many higher efficiency level schools, Chapter IV uses the non-parametric data envelopment analysis (DEA) approach of the super-efficiency model which has the ability to differentiate among the higher efficiency level schools. This model allows the efficient schools to have efficiency scores of more than one (in the traditional DEA approach, the efficiency score is bounded from zero to one). To investigate factors that potentially influence efficiency, this study performs the “second-stage” analysis by using bootstrapped quantile regression. The results suggest a number of policy implications for South-East Asian schools, indicating different courses of action for schools with higher and lower efficiency levels. Chapter V extends the analysis conducted in Chapter III both from methodological and empirical point of views. The analysis, based on the SFA approach, not only includes the ICT infrastructure in the model, but it also adds the ICT use (including the index of time spent by students in using ICT at school, outside school for entertainment purposes, and at home for school-related tasks). This is done by using the “four-component stochastic frontier model” where ICT is modeled both as inputs and determinants of time-varying inefficiency. In addition, this model is tested using a dataset of 24 OECD countries. Results show that all three variables belong to ICT use influence education outcomes, while as the determinants of time-varying inefficiency, these variables have only marginal effect on inefficiency. This study is then expected to provide a more holistic view of the role of ICT in the efficiency of education measurement as the previous studies only addressed the ICT infrastructure

Geo Data Science for Tourism

This reprint describes the recent challenges in tourism seen from the point of view of data science. Thanks to the use of the most popular Data Science concepts, you can easily recognise trends and patterns in tourism, detect the impact of tourism on the environment, and predict future trends in tourism. This reprint starts by describing how to analyse data related to the past, then it moves on to detecting behaviours in the present, and, finally, it describes some techniques to predict future trends. By the end of the reprint, you will be able to use data science to help tourism businesses make better use of data and improve their decision making and operations.

Doctor of Philosophy

dissertationChina’s retail sector has undertaken tremendous transformation since its opening to foreign investment in 1992. Retail transnational corporations have expanded rapidly in this emerging market. Yet relatively little is known about how they have embedded in the Chinese market and expanded spatially and temporally. China has experienced unprecedented urbanization since the onset of economic reform in 1978. Dramatic land use and land cover (LULC) change and urban expansion have taken place in the past three decades. Detailed time-series analysis of LULC change and urban growth in Chinese cities is still scant. This dissertation focuses on the expansion of foreign hypermarket retailers in China and the urban growth in one Chinese city, Suzhou. This research analyzes the penetration strategy and local embeddedness of foreign hypermarket retailers, examines their spatial inequality and dynamics at different geographical levels, and identifies their location determinants through binary logistic regression models. This study applies random forest classification to multitemporal Landsat Thematic Mapper (TM) images of Suzhou for LULC change analysis, employs landscape metrics and Geographic Information System (GIS) analysis to investigate urban growth patterns, and develops global and local logistic regression models to identify determinants of urban growth. The results indicate that spatiotemporal expansion of foreign hypermarket retailers has been largely dictated by the gradual liberalization policy of the Chinese government. Their local embeddedness has been impacted by both home and host economies. Relative gaps in foreign hypermarkets among three macro regions are narrowing while absolute gaps are widening. Provincial foreign hypermarket distribution has shown significant clustering in the Yangtze River Delta since 2005. Their distribution in Shanghai has changed from dispersion to intensified clustering and shown a clear trend of suburbanization. This study confirms that the random forest algorithm can effectively classify the heterogeneous landscape in Suzhou and LULC change has accelerated from 1986 to 2008. Three urban growth types, edge-expansion, infilling, and leapfrog are identified. Compared with the global model, the geographically weighted logistic regression model has overall better goodness-of-fit and provides more insights to spatial variations of the influence of underlying factors on urban growth

Predictive Modelling of Retail Banking Transactions for Credit Scoring, Cross-Selling and Payment Pattern Discovery

Evaluating transactional payment behaviour offers a competitive advantage in the modern payment ecosystem, not only for confirming the presence of good credit applicants or unlocking the cross-selling potential between the respective product and service portfolios of financial institutions, but also to rule out bad credit applicants precisely in transactional payments streams. In a diagnostic test for analysing the payment behaviour, I have used a hybrid approach comprising a combination of supervised and unsupervised learning algorithms to discover behavioural patterns. Supervised learning algorithms can compute a range of credit scores and cross-sell candidates, although the applied methods only discover limited behavioural patterns across the payment streams. Moreover, the performance of the applied supervised learning algorithms varies across the different data models and their optimisation is inversely related to the pre-processed dataset. Subsequently, the research experiments conducted suggest that the Two-Class Decision Forest is an effective algorithm to determine both the cross-sell candidates and creditworthiness of their customers. In addition, a deep-learning model using neural network has been considered with a meaningful interpretation of future payment behaviour through categorised payment transactions, in particular by providing additional deep insights through graph-based visualisations. However, the research shows that unsupervised learning algorithms play a central role in evaluating the transactional payment behaviour of customers to discover associations using market basket analysis based on previous payment transactions, finding the frequent transactions categories, and developing interesting rules when each transaction category is performed on the same payment stream. Current research also reveals that the transactional payment behaviour analysis is multifaceted in the financial industry for assessing the diagnostic ability of promotion candidates and classifying bad credit applicants from among the entire customer base. The developed predictive models can also be commonly used to estimate the credit risk of any credit applicant based on his/her transactional payment behaviour profile, combined with deep insights from the categorised payment transactions analysis. The research study provides a full review of the performance characteristic results from different developed data models. Thus, the demonstrated data science approach is a possible proof of how machine learning models can be turned into cost-sensitive data models