Using Web Data and Network Science to Detect Spatial Relationships

这个博士项目旨在使用最近开发的图形科学方法和技术来推动空间关系建模和解释的能力。通过将图形视为数据，反之亦然，它允许回答不同的统计问题，例如，当我们关心依赖关系时。动态网络嵌入方法是本项目将利用的一个最近增长的研究领域。该项目将使用一种基本上未被开发的数据来源--网络--来确定空间关系，对城市和地区之间的依存关系进行建模和预测。我们所说的空间关系是指两个地方如何连接，例如交通联系、贸易联系等。该项目旨在以新的方式在不同的空间和时间尺度上捕捉这种关系，同时将这些关系与背景联系起来，以回答有趣的社会科学问题。这里有机会将新的图形方法应用于实际生活中的重要数据，以及在图形方法中创建新的问题，该项目可以探索并努力回答这些问题。它使用新的数据驱动的想法来回答现有的问题，同时利用不断增长和可用的广泛丰富和海量的网络数据。我们将使用的数据的一个例子是JISC UK Web域数据集，这是一个新的地理位置、存档网站的缓存。它提供了一个有趣而有用的应用，新的数学理论可以应用于此。该项目将在使用数据科学的新方法时使用大数据来源。这是一个多学科项目，将解决地理学中存在的问题，同时也是将图论中发展的新想法应用于真实数据的机会；因此，该项目属于EPSRC统计和应用概率研究领域。这些发现可以被解释为更好地理解地方之间的关系和依赖是如何表现的，以便做出有用的推断和预测。它还将举例说明方法的有用性，同时强调分析返回的有趣见解并对其进行进一步研究。在利用这些网络数据创建关于城市和地区相互依存关系的有意义的地理知识、帮助更好地设计区域政策等方面，存在明显的差距。这个博士项目的愿景是应用新的图论来释放网络数据的未开发潜力，以捕捉否则我们无法理解的空间关系。该项目将积极影响我们对这些新方法的优点的理解，并突出潜在的扩展和发展机会。它能够为地理学家如何解释和研究空间关系提供一个新的视角。在使用网络数据时，它可以积极地影响我们如何能够以这样一种方式来查看数据，使其具有时间和空间方面，从而为其使用方式提供新的途径。

This PhD project aims to use recently developed Graph Science methodologies and techniques to drive forward the capabilities of modelling and interpreting spatial relationships. By viewing graphs as data and vice versa, it allows different statistical questions to be answered, for example when we care about dependencies. Dynamic network embedding methods are a research area with lots of recent growth that this project will utilise. This project will use a largely unexplored source of the data - the web - to identify spatial relationships, model and predict dependencies between cities and regions. By spatial relationships we mean how two places connect, e.g. transport links, trade links, etc. The project aims to capture such relationships in new ways, in various spatial and temporal scales, while contextualising these relationships to answer interesting social science questions. There is an opportunity here to apply new graphical methods to non trivial real life data, as well as creating new questions within the graph methodology that the project can explore and endeavour to answer. It uses new data-driven ideas to answer existing questions, while utilising widely rich and vast web data, that is ever growing and available. One example of data we will use is the JISC UK Web Domain Dataset, a novel cache of geolocated, archived websites. It presents an interesting and useful application for which new mathematical theory can be applied to. The project will use big data sources in using new approaches for data science. This is a multidisciplinary project that will tackle existing problems within Geography, whilst also being an opportunity for new ideas developing within Graph theory to be applied to real data; the project thus falls within the EPSRC Statistics and applied probability research area. The findings can be interpreted to better understand how relationships and dependencies between places behave, in order to make useful inferences and predictions. It will also exemplify the usefulness of the methodologies, while highlighting and conducting further research into interesting insights the analysis returns. There is an obvious gap in utilising such web data to create meaningful geographic knowledge about urban and regional interdependencies, to help better design things such as regional policies. The vision of this PhD project is to apply novel graph theory to release the untapped potential of web data to capture spatial relationships that otherwise we would not have been able to understand. The project will positively impact our understanding of the strengths of these new methods, as well as highlighting potential opportunities to expand and develop upon. It has the capability to give a new perspective on how geographers interpret and investigate spatial relationships. In using web data, it can positively impact how we are able to view data in such a way to give it a temporal and spatial aspect, that gives it new avenues in the ways it can be used.