Python for Smarter Cities: Comparison of Python libraries for static and interactive visualisations of large vector data

• URL: http://arxiv.org/abs/2202.13105v1
• Date: Sat, 26 Feb 2022 10:23:29 GMT
• Title: Python for Smarter Cities: Comparison of Python libraries for static and interactive visualisations of large vector data
• Authors: Gregor Herda, Robert McNabb
• Abstract summary: Python, with its concise and natural syntax, presents a low barrier to entry for municipal staff without computer science backgrounds. This study assesses prominent, actively-developed visualisation libraries in the Python ecosystem with respect to producing visualisations of large vector datasets. All short-listed libraries were able to generate the sample map products for both a small and larger dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Local governments, as part of 'smart city' initiatives and to promote interoperability, are increasingly incorporating open-source software into their data management, analysis, and visualisation workflows. Python, with its concise and natural syntax, presents a low barrier to entry for municipal staff without computer science backgrounds. However, with regard to geospatial visualisations in particular, the range of available Python libraries has diversified to such an extent that identifying candidate libraries for specific use cases is a challenging undertaking. This study therefore assesses prominent, actively-developed visualisation libraries in the Python ecosystem with respect to their suitability for producing visualisations of large vector datasets. A simple visualisation task common in urban development is used to produce near-identical thematic maps across static and an interactive 'tracks' of comparison. All short-listed libraries were able to generate the sample map products for both a small and larger dataset. Code complexity differed more strongly for interactive visualisations. Formal and informal documentation channels are highlighted to outline available resources for flattening learning curves. CPU runtimes for the Python-based portion of the process chain differed starkly for both tracks, pointing to avenues for further research. These results demonstrate that the Python ecosystem offers local governments powerful tools, free of vendor lock-in and licensing fees, to produce performant and consistently formatted visualisations for both internal and public distribution.

Related papers

• A Comprehensive Guide to Combining R and Python code for Data Science, Machine Learning and Reinforcement Learning [42.350737545269105]
  We show how to run Python's scikit-learn, pytorch and OpenAI gym libraries for building Machine Learning, Deep Learning, and Reinforcement Learning projects easily.
  arXiv  Detail & Related papers  (2024-07-19T23:01:48Z)
• Effective Design Verification -- Constrained Random with Python and Cocotb [2.3624953088402734]
  Python is the most widely used language across the world due to its simplicity and with 35 keywords (v3.7) The goal of this paper is to assess the effectiveness of a Python-Cocotb verification setup with design IPs and compare its features and performance metrics with the current de-facto hardware verification language i.e., SystemVerilog.
  arXiv  Detail & Related papers  (2024-05-06T07:58:16Z)
• CyNetDiff -- A Python Library for Accelerated Implementation of Network Diffusion Models [0.9831489366502302]
  CyNetDiff is a Python library with components written in Cython to provide improved performance for these computationally intensive diffusion tasks. In many research tasks, these simulations are the most computationally intensive task, so it would be desirable to have a library for these with an interface to a high-level language.
  arXiv  Detail & Related papers  (2024-04-25T21:59:55Z)
• pyvene: A Library for Understanding and Improving PyTorch Models via Interventions [79.72930339711478]
  $textbfpyvene$ is an open-source library that supports customizable interventions on a range of different PyTorch modules. We show how $textbfpyvene$ provides a unified framework for performing interventions on neural models and sharing the intervened upon models with others.
  arXiv  Detail & Related papers  (2024-03-12T16:46:54Z)
• PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series [0.0]
  PyPOTS is an open-source Python library dedicated to data mining and analysis on partially-observed time series. It provides easy access to diverse algorithms categorized into four tasks: imputation, classification, clustering, and forecasting.
  arXiv  Detail & Related papers  (2023-05-30T07:57:05Z)
• A Library for Representing Python Programs as Graphs for Machine Learning [39.483608364770824]
  We introduce an open source Python library python_graphs that applies static analysis to construct graph representations of Python programs. We present the capabilities and limitations of the library, perform a case study applying the library to millions of competitive programming submissions, and showcase the library's utility for machine learning research.
  arXiv  Detail & Related papers  (2022-08-15T22:36:17Z)
• PyGOD: A Python Library for Graph Outlier Detection [56.33769221859135]
  PyGOD is an open-source library for detecting outliers in graph data. It supports a wide array of leading graph-based methods for outlier detection. PyGOD is released under a BSD 2-Clause license at https://pygod.org and at the Python Package Index (PyPI)
  arXiv  Detail & Related papers  (2022-04-26T06:15:21Z)
• Interactive Visualization of Protein RINs using NetworKit in the Cloud [57.780880387925954]
  In this paper, we consider an example from protein dynamics, specifically residue interaction networks (RINs) We use NetworKit to build a cloud-based environment that enables domain scientists to run their visualization and analysis on large compute servers. To demonstrate the versatility of this approach, we use it to build a custom Jupyter-based widget for RIN visualization.
  arXiv  Detail & Related papers  (2022-03-02T17:41:45Z)
• Scikit-dimension: a Python package for intrinsic dimension estimation [58.8599521537]
  This technical note introduces textttscikit-dimension, an open-source Python package for intrinsic dimension estimation. textttscikit-dimension package provides a uniform implementation of most of the known ID estimators based on scikit-learn application programming interface. We briefly describe the package and demonstrate its use in a large-scale (more than 500 datasets) benchmarking of methods for ID estimation in real-life and synthetic data.
  arXiv  Detail & Related papers  (2021-09-06T16:46:38Z)
• pyBART: Evidence-based Syntactic Transformations for IE [52.93947844555369]
  We present pyBART, an easy-to-use open-source Python library for converting English UD trees to Enhanced UD graphs or to our representation. When evaluated in a pattern-based relation extraction scenario, our representation results in higher extraction scores than Enhanced UD, while requiring fewer patterns.
  arXiv  Detail & Related papers  (2020-05-04T07:38:34Z)
• OPFython: A Python-Inspired Optimum-Path Forest Classifier [68.8204255655161]
  This paper proposes a Python-based Optimum-Path Forest framework, denoted as OPFython. As OPFython is a Python-based library, it provides a more friendly environment and a faster prototyping workspace than the C language.
  arXiv  Detail & Related papers  (2020-01-28T15:46:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.