Learning Digital Geographies through Geographical Artificial Intelligence

As the distinction between online and physical spaces rapidly degrades, digital platforms have become an integral component of how people’s everyday experiences are mediated. User-generated content (UGC) shared on such platforms provides insights into how users want to represent their everyday lives, which augments and reinforces our understanding of local communities through time and layers dynamic information across and over the geographic space. Inspired by the development of the newly arisen scientific disciplines within geography: geographical artificial intelligence (GeoAI), this thesis adopts deep learning approaches on graph representations of human dynamics illustrated through geotagged UGC to explore how place representations are augmented and reinforced through users’ spatial experiences by classifying their multimedia activities and identifying the spatial clusters of UGC at the urban scale. Having the place representations described through UGC, this thesis explores how these representations can be used in conjunction with various official spatial statistics to understand and predict the dynamic changes of the socio-economic characteristics of places.The principal contributions of this thesis are: (1) to provide frameworks with higher classification and prediction accuracy but requiring fewer sample data; thus, contributing to an advanced framework to summarise spatial characteristics of places; (2) to show that multimedia content provides rich information regarding places, the use of space, and people’s experience of the landscape; thus, benefiting a better understanding of place representations; (3) to illustrate that the spatial patterns of UGC can be adopted as a valuable proxy to understand urban development and neighbourhood change; (4) to reinforce the concept that Spatial is Special. Spatial processes are commonly spatially autocorrelated. The mainstream of machine learning methods do not explicitly incorporate the spatial or spatio-temporal component to address such a speciality of spatial data. This thesis highlights the importance of explicitly incorporating spatial or spatio-temporal components in geographical analysis models.</div

A graph neural network framework for spatial geodemographic classification

Geodemographic classifications are exceptional tools for geographic analysis, business and policy-making, providing an overview of the socio-demographic structure of a region by creating an unsupervised, bottom-up classification of its areas based on a large set of variables. Classic approaches can require time-consuming preprocessing of input variables and are frequently a-spatial processes. In this study, we present a groundbreaking, systematic investigation of the use of graph neural networks for spatial geodemographic classification. Using Greater London as a case study, we compare a range of graph autoencoder designs with the official London Output Area Classification and baseline classifications developed using spatial fuzzy c-means. The results show that our framework based on a Node Attributes-focused Graph AutoEncoder (NAGAE) can perform similarly to classic approaches on class homogeneity metrics while providing higher spatial clustering. We conclude by discussing the current limitations of the proposed framework and its potential to develop into a new paradigm for creating a range of geodemographic classifications, from simple, local ones to complex classifications able to incorporate a range of spatial relationships into the process.</p

A graph-based semi-supervised approach to classification learning in digital geographies

As the distinction between online and physical spaces rapidly degrades, social media have now become an integral component of how many people's everyday experiences are mediated. As such, increasing interest has emerged in exploring how the content shared through those online platforms comes to contribute to the collaborative creation of places in physical space at the urban scale. Exploring digital geographies of social media data using methods such as qualitative coding (i.e., content labelling) is a flexible but complex task, commonly limited to small samples due to its impracticality over large datasets. In this paper, we propose a new tool for studies in digital geographies, bridging qualitative and quantitative approaches, able to learn a set of arbitrary labels (qualitative codes) on a small, manually-created sample and apply the same labels on a larger set. We introduce a semi-supervised, deep neural network approach to classify geo-located social media posts based on their textual and image content, as well as geographical and temporal aspects. Our innovative approach is rooted in our understanding of social media posts as augmentations of the time-space configurations that places are, and it comprises a stacked multi-modal autoencoder neural network to create joint representations of text and images, and a spatio-temporal graph convolution neural network for semi-supervised classification. The results presented in this paper show that our approach performs the classification of social media content with higher accuracy than traditional machine learning models as well as two state-of-art deep learning frameworks

Development of Solvent-Free Ambient Mass Spectrometry for Green Chemistry Applications

Green chemistry minimizes chemical process hazards in many ways, including eliminating traditional solvents or using alternative recyclable solvents such as ionic liquids. This concept is now adopted in this study for monitoring solvent-free reactions and analysis of ionic liquids, solids, and catalysts by mass spectrometry (MS), without using any solvent. In our approach, probe electrospray ionization (PESI), an ambient ionization method, was employed for this purpose. Neat viscous room-temperature ionic liquids (RTILs) in trace amounts (e.g., 25 nL) could be directly analyzed without sample carryover effect, thereby enabling high-throughput analysis. With the probe being heated, it can also ionize ionic solid compounds such as organometallic complexes as well as a variety of neat neutral solid chemicals (e.g., amines). More importantly, moisture-sensitive samples (e.g., [bmim]­[AlCl₄]) can be successfully ionized. Furthermore, detection of organometallic catalysts (including air-sensitive [Rh-MeDuPHOS]­[OTf]) in ionic liquids, a traditionally challenging task due to strong ion suppression effect from ionic liquids, can be enabled using PESI. In addition, PESI can be an ideal approach for monitoring solvent-free reactions. Using PESI-MS, we successfully examined the alkylation of amines by alcohols, the conversion of pyrylium into pyridinium, and the condensation of aldehydes with indoles as well as air- and moisture-sensitive reactions such as the oxidation of ferrocene and the condensation of pyrazoles with borohydride. Interestingly, besides the expected reaction products, the reaction intermediates such as the monopyrazolylborate ion were also observed, providing insightful information for reaction mechanisms. We believe that the presented solvent-free PESI-MS method would impact the green chemistry field

Learning urban form through unsupervised graph-convolutional neural networks

Graph theory has long provided the basis for the computa-tional  modelling  of  urban  flows  and  networks  and,  thus,  for  the  studyof urban form. The development of graph-convolutional neural networksoffers the opportunity to explore new applications of deep learning ap-proaches  in  urban  studies.  In  this  paper,  we  propose  an  unsupervisedgraph representation learning framework for analysing urban street net-works. Our results illustrate how a model trained on a 1% random sampleof street junctions in the UK can be used to explore the urban form of thecity of Leicester, generating embeddings which are similar but distinctfrom  classic  metrics  and  able  to  capture  key  aspects  such  as  the  shiftfrom urban to suburban structures. We conclude by outlining the cur-rent limitations and potential of the proposed framework for the studyof urban form and function.</p

Survival analyses of the training set of 142 stage I denocarcinomas.

<p>(A) Kaplan-Meier survival curves for two groups of patients with stage IA or IB. (B) Kaplan-Meier survival curves for the two groups of patients defined by having positive (high risk) or negative (low risk) risk scores of recurrence-free survival. The risk scores were estimated with 15 principle components based on the model using 51 recurrence-free survival-related genes. (C) The area under the curve (AUC) of time-dependent ROC analysis for survival models based on stage information or 51-gene expression data respectively. Time is indicated in months on the x-axis, cumulative survival is indicated on the y-axis. Tick marks, patients whose data were censored at last follow-up.</p

Genes related to tumor recurrence of stage I NSCLC.

<p>Genes related to tumor recurrence of stage I NSCLC.</p

Clinical summary of patients in the analyzed datasets.

<p>Clinical summary of patients in the analyzed datasets.</p

Top 30 significant prognostic KEGG pathways related to recurrence.

<p>Top 30 significant prognostic KEGG pathways related to recurrence.</p

Validation of the 51-gene signature in four independent datasets.

<p>Kaplan-Meier survival analysis was performed in low (<i>full red line</i>) and high (<i>dashed blue line</i>) risk patient groups defined by the 51-gene classifier. AUC for survival models based on stage (<i>dashed red line</i>) or 51-gene classifier (<i>full black line</i>) was also compared. The testing dataset GSE8894 do not have available stage information and all patients in the WUSTL dataset are stage IB. So the time dependent ROC using stage information in these two datasets could not be calculated; all set at 0.5 instead. Tick marks, patients whose data were censored at last follow-up.</p