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Public libraries provide equitable access to information for all citizens, and they play an important role in preserving and promoting culture, formal education and self-education, and enriching leisure time. Accordingly, there has been an increasing amount of research on the use factors and accessibility of public libraries, but research on the accessibility of public libraries in non-Western cities is insufficient compared to the corresponding research on other public facilities. In particular, in high-density cities such as Seoul, the Republic of Korea, it may be desirable in terms of sustainability to focus on the qualitative, rather than the quantitative, expansion of public libraries. In previous studies, the attractive factors on the supply side were analyzed using questionnaire surveys, but in this study, the attractive factors for users were quantified in the form of the library attraction index by means of user-generated contents such as location-based social media, and the accessibility was analyzed based on this. The results showed that many public libraries have high accessibility, with a high library attraction index. Therefore, these findings indicate that the qualitative expansion of public libraries is important for information equality. It is meaningful that this study analyzed the attractive factors on the supply side by analyzing the contents generated by users.
Jiyoung Kim; Jiwon Lee. An Analysis of Spatial Accessibility Changes According to the Attractiveness Index of Public Libraries Using Social Media Data. Sustainability 2021, 13, 9087 .
AMA StyleJiyoung Kim, Jiwon Lee. An Analysis of Spatial Accessibility Changes According to the Attractiveness Index of Public Libraries Using Social Media Data. Sustainability. 2021; 13 (16):9087.
Chicago/Turabian StyleJiyoung Kim; Jiwon Lee. 2021. "An Analysis of Spatial Accessibility Changes According to the Attractiveness Index of Public Libraries Using Social Media Data." Sustainability 13, no. 16: 9087.
Public bike-sharing is eco-friendly, connects excellently with other transportation modes, and provides a means of mobility that is highly suitable in the current era of climate change. This study proposes a methodology for inferring the bike trip purpose based on bike-share and point-of-interest (POI) data. Because the purpose of a trip involves decision-making, its inference necessitates an understanding of the spatiotemporal complexity of human activities. Thus, the spatiotemporal features affecting bike trips were selected from the bike-share data, and the land uses at the origin and destination of the trips were extracted from the POI data. During POI type embedding, the data were augmented considering the geographical distance between the POIs and the number of bike rentals at each bike station. We further developed a ground truth data construction method that uses temporal mobile and POI data. The inference model was built using machine learning and applied to experiments involving bike stations in Seocho-gu, Seoul, Korea. The experimental results revealed that optimal performance was achieved with the use of decision tree algorithms, as demonstrated by a 78.95% overall accuracy and 66.43% F1-score. The proposed method contributes to a better understanding of the causes of movement within cities.
Jiwon Lee; Kiyun Yu; Jiyoung Kim. Public Bike Trip Purpose Inference Using Point-of-Interest Data. ISPRS International Journal of Geo-Information 2021, 10, 352 .
AMA StyleJiwon Lee, Kiyun Yu, Jiyoung Kim. Public Bike Trip Purpose Inference Using Point-of-Interest Data. ISPRS International Journal of Geo-Information. 2021; 10 (5):352.
Chicago/Turabian StyleJiwon Lee; Kiyun Yu; Jiyoung Kim. 2021. "Public Bike Trip Purpose Inference Using Point-of-Interest Data." ISPRS International Journal of Geo-Information 10, no. 5: 352.
The increasing complexity of modern buildings has challenged the mobility of people with disabilities (PWD) in the indoor environment. To help overcome this problem, this paper proposes a data model that can be easily applied to indoor spatial information services for people with disabilities. In the proposed model, features are defined based on relevant regulations that stipulate significant mobility factors for people with disabilities. To validate the model’s capability to describe the indoor spaces in terms that are relevant to people with mobility disabilities, the model was used to generate data in a path planning application, considering two different cases in a shopping mall. The application confirmed that routes for people with mobility disabilities are significantly different from those of ordinary pedestrians, in a way that reflects features and attributes defined in the proposed data model. The latter can be inserted as an IndoorGML extension, and is thus expected to facilitate relevant data generation for the design of various services for people with disabilities.
Seula Park; Kiyun Yu; Jiyoung Kim. Data Model for IndoorGML Extension to Support Indoor Navigation of People with Mobility Disabilities. ISPRS International Journal of Geo-Information 2020, 9, 66 .
AMA StyleSeula Park, Kiyun Yu, Jiyoung Kim. Data Model for IndoorGML Extension to Support Indoor Navigation of People with Mobility Disabilities. ISPRS International Journal of Geo-Information. 2020; 9 (2):66.
Chicago/Turabian StyleSeula Park; Kiyun Yu; Jiyoung Kim. 2020. "Data Model for IndoorGML Extension to Support Indoor Navigation of People with Mobility Disabilities." ISPRS International Journal of Geo-Information 9, no. 2: 66.
Wearable and smartphone technology innovations have propelled the growth of Pedestrian Navigation Services (PNS). PNS need a map-matching process to project a user’s locations onto maps. Many map-matching techniques have been developed for vehicle navigation services. These techniques are inappropriate for PNS because pedestrians move, stop, and turn in different ways compared to vehicles. In addition, the base map data for pedestrians are more complicated than for vehicles. This article proposes a new map-matching method for locating Global Positioning System (GPS) trajectories of pedestrians onto road network datasets. The theory underlying this approach is based on the Fréchet distance, one of the measures of geometric similarity between two curves. The Fréchet distance approach can provide reasonable matching results because two linear trajectories are parameterized with the time variable. Then we improved the method to be adaptive to the positional error of the GPS signal. We used an adaptation coefficient to adjust the search range for every input signal, based on the assumption of auto-correlation between consecutive GPS points. To reduce errors in matching, the reliability index was evaluated in real time for each match. To test the proposed map-matching method, we applied it to GPS trajectories of pedestrians and the road network data. We then assessed the performance by comparing the results with reference datasets. Our proposed method performed better with test data when compared to a conventional map-matching technique for vehicles.
Yoonsik Bang; Jiyoung Kim; Kiyun Yu. An Improved Map-Matching Technique Based on the Fréchet Distance Approach for Pedestrian Navigation Services. Sensors 2016, 16, 1768 .
AMA StyleYoonsik Bang, Jiyoung Kim, Kiyun Yu. An Improved Map-Matching Technique Based on the Fréchet Distance Approach for Pedestrian Navigation Services. Sensors. 2016; 16 (10):1768.
Chicago/Turabian StyleYoonsik Bang; Jiyoung Kim; Kiyun Yu. 2016. "An Improved Map-Matching Technique Based on the Fréchet Distance Approach for Pedestrian Navigation Services." Sensors 16, no. 10: 1768.