@misc{2010Clustergram,
  title      = {Clustergram: Visualization and Diagnostics for Cluster Analysis ({{R}} Code) | {{R-statistics}} Blog},
  shorttitle = {Clustergram},
  author     = {Galili, Tal},
  year       = {2010},
  month      = jun,
  abstract   = {About Clustergrams In 2002, Matthias Schonlau published in "The Stata Journal" an article named "The Clustergram: A graph for visualizing hierarchical and . As explained in the abstract: In hierarchical cluster analysis dendrogram graphs are used to visualize how clusters are formed. I propose an alternative graph named ``clustergram'' to examine how cluster members are},
  langid     = {american}
}

@article{2020SciPy-NMeth,
  title   = {{{SciPy}} 1.0: {{Fundamental}} Algorithms for Scientific Computing in Python},
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and {van der Walt}, St{\'e}fan J. and Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and Kern, Robert and Larson, Eric and Carey, C J and Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and VanderPlas, Jake and Laxalde, Denis and Perktold, Josef and Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and Harris, Charles R. and Archibald, Anne M. and Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  year    = {2020},
  journal = {Nature Methods},
  volume  = {17},
  pages   = {261--272},
  doi     = {10/ggj45f},
  adsurl  = {https://rdcu.be/b08Wh}
}

@article{arribas-bel2022Spatiala,
  title    = {Spatial {{Signatures}} - {{Understanding}} (Urban) Spaces through Form and Function},
  author   = {{Arribas-Bel}, Daniel and Fleischmann, Martin},
  year     = {2022},
  month    = oct,
  journal  = {Habitat International},
  volume   = {128},
  pages    = {102641},
  issn     = {0197-3975},
  doi      = {10.1016/j.habitatint.2022.102641},
  abstract = {This paper presents the notion of spatial signatures as a characterisation of space based on form and function designed to understand urban environments. The spatial configuration of the dif-ferent components of cities is relevant for at least two main reasons. On the one hand, it encodes many aspects of the phenomena that created such an arrangement in the first place. On the other, once in place, this arrangement of urban form and function underpins many outcomes, from economic productivity to environmental sustainability. Our approach unfolds in three main stages. First, we propose a new spatial unit \textendash the Enclosed Tessellation (ET) cell\textendash{} to delineate space in a way that is exhaustive and matches the underlying processes at which urban form and function operate. Second, we propose to attach a large variety of form and function-based characters to ET cells to describe each of these units. Third, to build spatial signatures, information on ET cells can be clustered using unsupervised learning techniques. This process results in a theory-informed, data-driven typology of space that follows form and function. We demonstrate the flexibility of the approach to a variety of data landscapes and cultural backgrounds by providing five illustrations of spatial signatures for five cities across five continents. These showcases demonstrate the ability to successfully differentiate areas of a city that were built at different points in time and under different technological regimes, but also highlight broader comparisons about the nature of urban fabric in different regions of the world. Our contribution resides in leveraging modern data, tech-nology and methods to propose a detailed, consistent and scalable methodology that characterises urban form and function. The spatial signatures can be used across academic disciplines and by a variety of practitioners and policymakers supporting initiatives such as the Sustainable Development Goals.},
  langid   = {english},
  keywords = {Geographic data science,Urban form,Urban function}
}

@misc{bokehteamBokeh,
  title      = {Bokeh/Bokeh: {{Interactive Data Visualization}} in the Browser, from {{Python}}},
  shorttitle = {Bokeh/Bokeh},
  author     = {{Bokeh Team}},
  year       = {2023}
}

@article{fleischmann2022Geographicala,
  title     = {Geographical Characterisation of {{British}} Urban Form and Function Using the Spatial Signatures Framework},
  author    = {Fleischmann, Martin and {Arribas-Bel}, Daniel},
  year      = {2022},
  month     = sep,
  journal   = {Scientific Data},
  volume    = {9},
  number    = {1},
  pages     = {546},
  publisher = {{Nature Publishing Group}},
  issn      = {2052-4463},
  doi       = {10.1038/s41597-022-01640-8},
  abstract  = {The spatial arrangement of the building blocks that make up cities matters to understand the rules directing their dynamics. Our study outlines the development of the national open-source classification of space according to its form and function into a single typology. We create a bespoke granular spatial unit, the enclosed tessellation, and measure characters capturing its form and function within a relevant spatial context. Using K-Means clustering of individual enclosed tessellation cells, we generate a classification of space for the whole of Great Britain. Contiguous enclosed tessellation cells belonging to the same class are merged forming spatial signature geometries and their typology. We identify 16 distinct types of spatial signatures stretching from wild countryside, through various kinds of suburbia to types denoting urban centres according to their regional importance. The open data product presented here has the potential to serve as boundary delineation for other researchers interested in urban environments and policymakers looking for a unique perspective on cities and their structure.},
  copyright = {2022 The Author(s)},
  langid    = {english},
  keywords  = {Geography,Society}
}

@article{hunter2007Matplotlib,
  title      = {Matplotlib: {{A 2D Graphics Environment}}},
  shorttitle = {Matplotlib},
  author     = {Hunter, John D.},
  year       = {2007},
  month      = may,
  journal    = {Computing in Science \& Engineering},
  volume     = {9},
  number     = {3},
  pages      = {90--95},
  issn       = {1558-366X},
  doi        = {10.1109/MCSE.2007.55},
  abstract   = {Matplotlib is a 2D graphics package used for Python for application development, interactive scripting,and publication-quality image generation across user interfaces and operating systems},
  keywords   = {application development,Computer languages,Equations,Graphical user interfaces,Graphics,Image generation,Interpolation,Operating systems,Packaging,Programming profession,Python,scientific programming,scripting languages,User interfaces}
}

@article{ma2022Abstract,
  title      = {Abstract 5038: {{Applying}} Reproducible Genomic Data Science Methods for the Analysis of a Rare Tumor Type},
  shorttitle = {Abstract 5038},
  author     = {Ma, Li and Peterson, Erich and Steliga, Mathew and Muesse, Jason and Marino, Katy and Arnaoutakis, Konstantinos and Shin, Ikjae and Johann, Donald J.},
  year       = {2022},
  month      = jun,
  journal    = {Cancer Research},
  volume     = {82},
  number     = {12\_Supplement},
  pages      = {5038},
  issn       = {0008-5472},
  doi        = {10.1158/1538-7445.AM2022-5038},
  abstract   = {Background: The new and emerging discipline of data science demands reproducibility, which is vital in science and presents a significant challenge for high throughput genomics. To further complicate matters, large and complex projects require collaboration by multiple investigators examining and analyzing the massive data of multiple genomic modalities from different perspectives. Today, researchers are rarely able to reproduce published genomic studies for a variety of reasons, for example: i) differences between versions of software used, ii) lack of detail regarding software parameters, iii) lack of data access, and iv) source code not provided. Here we combine our data infrastructure approach with a molecular infrastructure and apply it to the exploration of a multimodality genomic analysis of a patient with a pulmonary pneumocytoma.Methods: Open source methods are utilized that include the SQLite database with R and python packages and custom code.Results: Our approach is able to generate a wide variety of plots and tables for the purposes of exploratory data analysis (EDA) and/or other user-specific analyses, such as finding differentially expressed genes (DEGs). In addition to traditional EDA plots, the R library RCircos is used to visualize multiple NGS studies (eg, 7) in single plot. Differential gene expression (DGE) analysis takes normalized RNA-based read count data and performs a statistical analysis, to find quantitative changes in expression levels between different experimental groups. A DGE analysis report is routinely generated. An abbreviated copy number variation (CNV) report derived from an ultra-low-pass whole genome (tumor/germline) NGS approach is also generated. A Python/Jupyter Notebook utilizing a library from scikit-learn is used to generate a clustergram plot. This approach is used as part of finding the optimal number of clusters for a K-Means analysis. RNA-seq data normalized across three sample types using DESeq2 were used in this example. Finally, advanced pathway analysis is performed for the identification of activated and deactivated molecular pathways.Conclusion: The next evolution in oncology research and cancer care are being driven by data science. In the field of genomic data science, accuracy and reproducibility remains a considerable challenge due to the sheer size, complexity, and dynamic nature of the experimental data plus relative inventiveness of the quantitative biology approaches. The accuracy and reproducibility challenge does not just block the path to new scientific discoveries, more importantly, it may lead to a scenario where critical findings used for medical decision making are found to be incorrect. Our approach has been developed to meet the unmet need of improving accuracy and reproducibility in genomic data science. Specific findings related to the rare pneumocytoma tumor will be presented.Citation Format: Li Ma, Erich Peterson, Mathew Steliga, Jason Muesse, Katy Marino, Konstantinos Arnaoutakis, Ikjae Shin, Donald J. Johann. Applying reproducible genomic data science methods for the analysis of a rare tumor type [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12\_Suppl):Abstract nr 5038.}
}

@article{samardzhiev2022Functionala,
  title      = {Functional Signatures in {{Great Britain}}: {{A}} Dataset},
  shorttitle = {Functional Signatures in {{Great Britain}}},
  author     = {Samardzhiev, Krasen and Fleischmann, Martin and {Arribas-Bel}, Daniel and Calafiore, Alessia and Rowe, Francisco},
  year       = {2022},
  month      = aug,
  journal    = {Data in Brief},
  volume     = {43},
  pages      = {108335},
  issn       = {2352-3409},
  doi        = {10.1016/j.dib.2022.108335},
  abstract   = {The spatial distribution of activities and agents within cities, conceptualised as an urban function, profoundly affects how different areas are perceived and lived. This dataset introduces the concept of functional signatures - contiguous areas of a similar urban function delineated based on enclosed tessellation cells (ETC) - and applies it to the area of Great Britain. ETCs are granular spatial units, which capture function based on interpolations from open data inputs stretching from remote sensing to land use, census and points of interest data. The spatial extent of each signature type is defined by grouping ETCs using cluster analysis, based on similarity between their functional profiles, inferred by the data linked to each cell. This approach results in a dataset that reflects urban function as a composite of aspects, rather than a singular use, and is built up from granular spatial units. Furthermore, the underlying data are sourced from available open data products, which together with a method and code fully available, yields a fully reproducible pipeline and makes our dataset and open data product. Both the final classification composed of 17 types of functional signatures and the underlying data collected on the level of enclosed tessellation cells are included in the release and described in this report.},
  langid     = {english},
  keywords   = {Functional areas,Geographic data science,Land use,Spatial data,Urban analytics}
}

@article{schonlau2002clustergram,
  title     = {The Clustergram: {{A}} Graph for Visualizing Hierarchical and Nonhierarchical Cluster Analyses},
  author    = {Schonlau, Matthias},
  year      = {2002},
  journal   = {The Stata Journal},
  volume    = {2},
  number    = {4},
  pages     = {391--402},
  publisher = {{SAGE Publications Sage CA: Los Angeles, CA}},
  doi       = {10/ghh97z}
}

@article{schonlau2004Visualizing,
  title    = {Visualizing Non-Hierarchical and Hierarchical Cluster Analyses with Clustergrams},
  author   = {Schonlau, Matthias},
  year     = {2004},
  month    = feb,
  journal  = {Computational Statistics},
  volume   = {19},
  number   = {1},
  pages    = {95--111},
  issn     = {1613-9658},
  doi      = {10.1007/BF02915278},
  abstract = {In hierarchical cluster analysis dendrogram graphs are used to visualize how clusters are formed. Because each observation is displayed dendrograms are impractical when the data set is large. For non-hierarchical cluster algorithms (e.g. Kmeans) a graph like the dendrogram does not exist. This paper discusses a graph named ``clustergram'' to examine how cluster members are assigned to clusters as the number of clusters increases. The clustergram can also give insight into algorithms. For example, it can easily be seen that the ``single linkage'' algorithm tends to form clusters that consist of just one observation. It is also useful in distinguishing between random and deterministic implementations of the Kmeans algorithm. A data set related to asbestos claims and the Thailand Landmine Data are used throughout to illustrate the clustergram.},
  langid   = {english},
  keywords = {cluster analysis,Dendrogram,Kmeans algorithm,large data,non-hierarchical,tree}
}

@article{scikit-learn,
  title   = {Scikit-Learn: {{Machine}} Learning in {{Python}}},
  author  = {Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  year    = {2011},
  journal = {Journal of Machine Learning Research},
  volume  = {12},
  pages   = {2825--2830}
}

@article{singleton2022Estimatinga,
  title    = {Estimating Generalized Measures of Local Neighbourhood Context from Multispectral Satellite Images Using a Convolutional Neural Network},
  author   = {Singleton, Alex and {Arribas-Bel}, Dani and Murray, John and Fleischmann, Martin},
  year     = {2022},
  month    = jul,
  journal  = {Computers, Environment and Urban Systems},
  volume   = {95},
  pages    = {101802},
  issn     = {0198-9715},
  doi      = {10.1016/j.compenvurbsys.2022.101802},
  abstract = {The increased availability of high-resolution multispectral imagery captured by remote sensing platforms provides new opportunities for the characterisation and differentiation of urban context. The discovery of generalized latent representations from such data are however under researched within the social sciences. As such, this paper exploits advances in machine learning to implement a new method of capturing measures of urban context from multispectral satellite imagery at a very small area level through the application of a convolutional autoencoder (CAE). The utility of outputs from the CAE is enhanced through the application of spatial weighting, and the smoothed outputs are then summarised using cluster analysis to generate a typology comprising seven groups describing salient patterns of differentiated urban context. The limits of the technique are discussed with reference to the resolution of the satellite data utilised within the study and the interaction between the geography of the input data and the learned structure. The method is implemented within the context of Great Britain, however, is applicable to any location where similar high resolution multispectral imagery are available.},
  langid   = {english},
  keywords = {Cluster analysis,Convolutional neural networks,Deep learning,Multispectral satellite imagery,Urban morphology}
}

@inproceedings{stewart2022Addressing,
  title     = {Addressing {{Data Gaps}} for {{Facility Reliability Assessments Using Non-Hierarchical Cluster Analysis}}},
  booktitle = {2022 14th {{International Pipeline Conference}}},
  author    = {Stewart, Ryan and Di Blasi, Martin and Dessein, Thomas},
  year      = {2022},
  month     = dec,
  publisher = {{American Society of Mechanical Engineers Digital Collection}},
  doi       = {10.1115/IPC2022-87145},
  abstract  = {Abstract. Performing reliability assessments for a large asset inventory of pipeline facility equipment, such as compressor station assets, requires a substantial dataset of attributes for a diverse range of equipment types. In many cases, equipment data inventories have gaps, with one or more required attributes unknown, such as diameter, wall thickness, operating pressure or material properties. The identification and collection of complete records is typically labor-intensive and time consuming, so data gaps are often filled with assumptions while ongoing data collection improves. A standard approach to fill these gaps is to use conservative assumptions for missing attributes. This results in missing data producing higher assessed risk than complete records. The benefit of this conservative approach is that it appropriately penalizes the incomplete records, driving action toward collecting the information where it matters. However, this approach is simple, does not leverage all the information available within the available dataset, and can produce a distorted representation of risk that may reduce the credibility of the risk assessment.This paper describes a process to use unsupervised machine learning algorithms to organize large asset inventories into groups and fill data gaps with reasonable, but conservative assumptions. We used a non-hierarchical clustering method to group asset records into clusters. Instead of using the most conservative value to fill data gaps across all records, gaps are filled using the most conservative value from similar records. This method provides estimates for data gaps that are more realistic while still maintaining conservatism, striking a balance between prioritizing equipment with confirmed attributes that indicate higher risk and equipment with little information.The approach described in this study relies on K-means clustering. We discuss the practical uses of dimensionality reduction, heuristic techniques for selecting the number of clusters, and sensitivity analysis.},
  langid    = {english}
}

@article{urbanski2022Monitoring,
  title    = {Monitoring and Classification of High {{Arctic}} Lakes in the {{Svalbard Islands}} Using Remote Sensing},
  author   = {Urba{\'n}ski, Jacek Andrzej},
  year     = {2022},
  month    = aug,
  journal  = {International Journal of Applied Earth Observation and Geoinformation},
  volume   = {112},
  pages    = {102911},
  issn     = {1569-8432},
  doi      = {10.1016/j.jag.2022.102911},
  abstract = {Arctic lakes are an essential element of the environment. This study inventoried and classified lakes on three of the Svalbard Islands (Spitsbergen, Barents\o ya, and Edge\o ya) using Sentinel-2 images. The Forel-Ule color index (FUI) was employed to measure the lake's color, and a new equation to calculate the concentration of suspended sediment using satellite images was developed and tested using measurements from coastal waters. These two measures, plus the normalized difference vegetation index for vegetation on the lakeshore, and the lakes' location were used for clustering and classification. More than 1000 natural lakes were found on the three Svalbard islands. Nearly 80~\% of these were smaller than 0.01~km2 (1~ha), with the largest a few km2 in area. Lake distribution analysis identified six zones of increased lake density, five of which were located along the western coast of Spitsbergen. The FUI was thus demonstrated to be useful for the monitoring of Arctic lakes. The K-means clustering results and analysis of the spectral signatures divided the lakes into seven classes: clear water, moderately clear water, tundra, coastal tundra, meltwater, extreme meltwater, and supraglacial lakes. Meltwater and clear water lakes were the two most common types, accounting for about 80~\% of the total lake area.},
  langid   = {english},
  keywords = {Arctic lakes,Forel-Ulle index,Lakes classification,Satellite monitoring,Svalbard}
}
@article{raschka2020machine,
  title   = {Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence},
  author  = {Raschka, Sebastian and Patterson, Joshua and Nolet, Corey},
  journal = {arXiv preprint arXiv:2002.04803},
  year    = {2020}
}
@manual{palmerpenguins,
  title  = {palmerpenguins: Palmer Archipelago (Antarctica) penguin data},
  author = {Allison Marie Horst and Alison Presmanes Hill and Kristen B Gorman},
  year   = {2020},
  note   = {R package version 0.1.0},
  doi    = {10.5281/zenodo.3960218},
  url    = {https://allisonhorst.github.io/palmerpenguins/}
}
@article{yang2022Ageing,
  title      = {Ageing in {{Place Classification}}: {{Creating}} a Geodemographic Classification for~the Ageing Population in {{England}}},
  shorttitle = {Ageing in {{Place Classification}}},
  author     = {Yang, Yuanxuan and Dolega, Les and {Darlington-Pollock}, Fran},
  year       = {2022},
  month      = dec,
  journal    = {Applied Spatial Analysis and Policy},
  issn       = {1874-4621},
  doi        = {10.1007/s12061-022-09490-y},
  abstract   = {Population ageing is one of the most significant demographic changes underway in many countries. Far from being a homogenous group, older people and their experiences of ageing are diverse. A better understanding of the characteristics and geography of the older population, including the older workforce, is important. It allows policymakers and stakeholders to better adapt to the opportunities and challenges that the ageing population brings. This paper describes the implementation of the Ageing in Place Classification (AiPC) in England. AiPC is a multidimensional geodemographic classification, and it employs a wide range of spatially representative attributes of older people's sociodemographic characteristics and their living environment at the small area level. The openly available product provides valuable insights that can be implemented in both local and national contexts, in particular to improve service delivery and inform targeted policy interventions. AiPC is readily updateable with the arrival of new Census data; the concept and framework are also transferable to other countries.},
  langid     = {english},
  keywords   = {Ageing,Area classification,Geodemographics,Population}
}

@inproceedings{sklearn_api,
  author    = {Lars Buitinck and Gilles Louppe and Mathieu Blondel and
               Fabian Pedregosa and Andreas Mueller and Olivier Grisel and
               Vlad Niculae and Peter Prettenhofer and Alexandre Gramfort
               and Jaques Grobler and Robert Layton and Jake VanderPlas and
               Arnaud Joly and Brian Holt and Ga{\"{e}}l Varoquaux},
  title     = {{API} design for machine learning software: experiences from the scikit-learn
               project},
  booktitle = {ECML PKDD Workshop: Languages for Data Mining and Machine Learning},
  year      = {2013},
  pages     = {108--122}
}

@manual{ecotone,
  title  = {EcotoneFinder: Characterising and Locating Ecotones and Communities},
  author = {Antoine Bagnaro},
  year   = {2021},
  note   = {R package version 0.2.3},
  url    = {https://cran.r-project.org/web/packages/EcotoneFinder/index.html}
}

@article{GAGOLEWSKI2021620,
title = {Are cluster validity measures (in) valid?},
journal = {Information Sciences},
volume = {581},
pages = {620-636},
year = {2021},
issn = {0020-0255},
doi = {https://doi.org/10.1016/j.ins.2021.10.004},
url = {https://www.sciencedirect.com/science/article/pii/S0020025521010082},
author = {Marek Gagolewski and Maciej Bartoszuk and Anna Cena},
keywords = {Clustering methodology, Cluster validity index, Dunn index, Nearest neighbours (NNs), Ordered weighted averaging (OWA) operator, No free lunch},
abstract = {Internal cluster validity measures (such as the Calinski–Harabasz, Dunn, or Davies–Bouldin indices) are frequently used for selecting the appropriate number of partitions a dataset should be split into. In this paper we consider what happens if we treat such indices as objective functions in unsupervised learning activities. Is the optimal grouping with regards to, say, the Silhouette index really meaningful? It turns out that many cluster (in)validity indices promote clusterings that match expert knowledge quite poorly. We also introduce a new, well-performing variant of the Dunn index that is built upon OWA operators and the near-neighbour graph so that subspaces of higher density, regardless of their shapes, can be separated from each other better.}
}