Lansley, G., & Longley, P. A. (2016). The geography of Twitter topics in London. Computers, Environment and Urban Systems, 58, 85–96.
더보기이 연구는 런던에서 발생한 트위터 데이터를 분석하여 트위터의 주제별 지리적 분포를 탐구합니다. 즉, 런던 내에서 사람들이 트위터를 통해 어떤 주제들에 대해 언급하는지, 그리고 그 주제들이 특정 지역에서 어떻게 다르게 나타나는지를 분석합니다.연구의 주요 내용:목표:런던 내에서 트위터의 다양한 주제들이 어떤 지역에서 더 많이 언급되는지 파악하는 것.주제별 트윗의 지리적 분포를 분석하여 도시 내에서 어떤 주제가 지역적으로 집중되는지, 그리고 이를 통해 도시의 사회적, 문화적 특성을 어떻게 이해할 수 있는지 탐구.데이터:연구에서는 2014년과 2015년에 런던에서 발생한 트위터 데이터를 사용합니다. 트위터의 위치 기반 데이터를 활용하여 트윗이 작성된 지역을 분석하고, 각 트윗이 언급하는 주제를 식별하기 위..
2024. 10. 15.
Gao, S., Liu, Y., Wang, Y., & Ma, X. (2013). Discovering Spatial Interaction Communities from Mobile Phone D ata. Transactions in GIS, 17(3), 463–481.
1 IntroductionThere are still some important questions that deserve attention: for example, what is the relationship between interaction in the physical world and that in cyberspace, how to exploit and analyze patterns of spatial interaction, and how to design efficient economical and administrative boundaries based on spatial interaction?Spatialized social network analysis (SSNA) facilitates th..
2024. 10. 15.
Liu, Y., Liu, X., Gao, S., Gong, L., Kang, C., Zhi, Y., ... & Shi, L. (2015). Social sensing: A new approach to understanding our socioeconomic environments. Annals of the Association of American Geographers, 105(3), 512-530.
AbstractThis paper defines social sensing as individual-level spatiotemporally tagged data and related methodologies. This paper explains the similarity between social sensing data and remote sensing data, and the possibility of combining those datasets The major objective of social sensing is to detect socioeconomic characteristics in geographical space, which can thus be viewed as a complement..
2024. 9. 29.
Koehler, M., & Mishra, P. (2009). What is technological pedagogical content knowledge (TPACK)?. Contemporary issues in technology and teacher education, 9(1), 60-70.
Abstract- The TPACK framework is described in detail, as a complex interaction among three bodies of knowledge: Content, pedagogy, and technology. The Challenges of Teaching With Technology- Newer digital technologies, which are protean, unstable, and opaque, present new challenges to teachers struggling to use more technology in their teaching- Social and contextual factors also complicate the ..
2024. 9. 26.
Toohey, K., & Duckham, M. (2015). Trajectory similarity measures. Sigspatial Special, 7(1), 43-50.
1 Introduction- After first outlining some of the useful applications of trajectory similarity measures, four of the most commonly used similarity measures will be discussed in detail: longest common subsequence (LCSS), Fréchet distance, dynamic time warping (DTW), and edit distance- These four measures have been implemented within a new R package called “SimilarityMeasures,” available on CRAN 2..
2024. 9. 23.
Xu, Y., Shaw, S. L., Zhao, Z., Yin, L., Lu, F., Chen, J., ... & Li, Q. (2018). Another tale of two cities: Understanding human activity space using actively tracked cellphone location data. In Geographies of mobility (pp. 246-258). Routledge.
Intro- Activity space is an important concept in geography that describes the spatial extent, frequent locations, and movements of people’s daily activities Literature rivew- activity space- activity space using cellphone location data- clustering methods Study area and data sets- Shanghai and Shenzhen- This article uses two actively tracked cellphone data sets collected weekly in Shenzhen (23 M..
2024. 9. 17.
Zhao, Z., Shaw, S. L., Xu, Y., Lu, F., Chen, J., & Yin, L. (2016). Understanding the bias of call detail records in human mobility research. International Journal of Geographical Information Science, 30(9), 1738-1762.
1. Introduction- There have been debates regarding the biases that come with the geo-tagged social media data.- Call detail records (CDRs) were most widely used in existing studies.- However, most previous studies did not discuss how representative their data were and the applicability of their analysis results to the entire population. Therefore, the representativeness of CDRs needs to be exami..
2024. 9. 2.
Yuan, Y., Lu, Y., Chow, T. E., Ye, C., Alyaqout, A., & Liu, Y. (2021). The missing parts from social media-enabled smart cities: who, where, when, and what?. In Smart Spaces and Places (pp. 130-142). Routledge.
Introduction- The increase in social networking sites (SNS) introduced a new opportunity to monitor people's activities and the perception of their surroundings. Location-Based Social Media (LBSM) has been used as a potential resource to characterize social perceptions of places and to model human activities.- However, LBSM data have various data quality issues such as accuracy, precision, compl..
2024. 8. 29.
Cheng ,T., Haworth, J., & Ozkan, M. C. (2024). 12. Spatiotemporal AI for Transportation
12. Spatiotemporal AI for Transportation 12.1 Introduction: background on spatiotemporal AI and transportation- 교통 데이터에는 flows and volumes, travel times, trajectories, public transit trips 등이 포함됨- nonlinearity, heterogeneity, nonstationarity 때문에 M/L(e.g. neural networks, kernel methods, random forests 등)으로 연구 이동- 교통 연구의 목적은 교통 예측 12.2 Data-driven prediction of traffic variables- 교통 연구의 기본 접근은 교통..
2024. 8. 20.
