splot - visual analytics for spatial statistics

We closed a total of 15 issues (enhancements and bug fixes) through 6 pull requests, since our last release on 2020-01-18. Issues Closed add permanent links to current version of no's to joss paper (#102) [BUG] set colors as list in _plot_choropleth_fig() (#101) Remove the links around figures in the JOSS paper (#99) Release prep for 1.1.2 (#98) Installation instructions; pip install fails on macOS (#88) Usage in readme is a fragment (#90) JOSS: missing figure captions (#92) [DOC] update installation instruction (#96) [DOC] add example links to README.md & figure captions in joss article (#97) Pull Requests add permanent links to current version of no's to joss paper (#102) [BUG] set colors as list in _plot_choropleth_fig() (#101) Remove the links around figures in the JOSS paper (#99) Release prep for 1.1.2 (#98) [DOC] update installation instruction (#96) [DOC] add example links to README.md & figure captions in joss article (#97) The following individuals contributed to this release: Stefanie Lumnitz Levi John Wolf Leonardo Uieda Serge ReyJoss paper releas

A Data-driven, High-performance and Intelligent CyberInfrastructure to Advance Spatial Sciences

abstract: In the field of Geographic Information Science (GIScience), we have witnessed the unprecedented data deluge brought about by the rapid advancement of high-resolution data observing technologies. For example, with the advancement of Earth Observation (EO) technologies, a massive amount of EO data including remote sensing data and other sensor observation data about earthquake, climate, ocean, hydrology, volcano, glacier, etc., are being collected on a daily basis by a wide range of organizations. In addition to the observation data, human-generated data including microblogs, photos, consumption records, evaluations, unstructured webpages and other Volunteered Geographical Information (VGI) are incessantly generated and shared on the Internet. Meanwhile, the emerging cyberinfrastructure rapidly increases our capacity for handling such massive data with regard to data collection and management, data integration and interoperability, data transmission and visualization, high-performance computing, etc. Cyberinfrastructure (CI) consists of computing systems, data storage systems, advanced instruments and data repositories, visualization environments, and people, all linked together by software and high-performance networks to improve research productivity and enable breakthroughs that are not otherwise possible. The Geospatial CI (GCI, or CyberGIS), as the synthesis of CI and GIScience has inherent advantages in enabling computationally intensive spatial analysis and modeling (SAM) and collaborative geospatial problem solving and decision making. This dissertation is dedicated to addressing several critical issues and improving the performance of existing methodologies and systems in the field of CyberGIS. My dissertation will include three parts: The first part is focused on developing methodologies to help public researchers find appropriate open geo-spatial datasets from millions of records provided by thousands of organizations scattered around the world efficiently and effectively. Machine learning and semantic search methods will be utilized in this research. The second part develops an interoperable and replicable geoprocessing service by synthesizing the high-performance computing (HPC) environment, the core spatial statistic/analysis algorithms from the widely adopted open source python package – Python Spatial Analysis Library (PySAL), and rich datasets acquired from the first research. The third part is dedicated to studying optimization strategies for feature data transmission and visualization. This study is intended for solving the performance issue in large feature data transmission through the Internet and visualization on the client (browser) side. Taken together, the three parts constitute an endeavor towards the methodological improvement and implementation practice of the data-driven, high-performance and intelligent CI to advance spatial sciences.Dissertation/ThesisDoctoral Dissertation Geography 201

Machine learning for particle identification in the LHCb detector

LHCb experiment is a specialised b-physics experiment at the Large Hadron Collider at CERN. It has a broad physics program with the primary objective being the search for CP violations that would explain the matter-antimatter asymmetry of the Universe. LHCb studies very rare phenomena, making it necessary to process millions of collision events per second to gather enough data in a reasonable time frame. Thus software and data analysis tools are essential for the success of the experiment. Particle identification (PID) is a crucial ingredient of most of the LHCb results. The quality of the particle identification depends a lot on the data processing algorithms. This dissertation aims to leverage the recent advances in machine learning field to improve the PID at LHCb. The thesis contribution consists of four essential parts related to LHCb internal projects. Muon identification aims to quickly separate muons from the other charged particles using only information from the Muon subsystem. The second contribution is a method that takes into account a priori information on label noise and improves the accuracy of a machine learning model for classification of this data. Such data are common in high-energy physics and, in particular, is used to develop the data-driven muon identification methods. Global PID combines information from different subdetectors into a single set of PID variables. Cherenkov detector fast simulation aims to improve the speed of the PID variables simulation in Monte-Carlo

Digital Forensics Tools Integration

As technology has become pervasive in our lives we record our daily activities both intentionally and unintentionally. Because of this, the amount of potential evidence found on digital media is staggering. Investigators have had to adapt and change their methods of conducting investigations to address the data volume. Digital forensics examiners current process consists of performing string searches to identify potential evidentiary items. Items of interest must then go through association, target comparison, and event reconstruction processes. These are manual and time consuming tasks for an examiner. This thesis presents a user interface that combines both the string searching capabilities that begin an investigation with automated correlation and abstraction into a single timeline visualization. The capability to improve an examiner\u27s process is evaluated on the tools ability to reduce the number of results to sort through while accurately presenting key items for three use cases

Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes

Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes

Remote Sensing of Plant Biodiversity

This Open Access volume aims to methodologically improve our understanding of biodiversity by linking disciplines that incorporate remote sensing, and uniting data and perspectives in the fields of biology, landscape ecology, and geography. The book provides a framework for how biodiversity can be detected and evaluated—focusing particularly on plants—using proximal and remotely sensed hyperspectral data and other tools such as LiDAR. The volume, whose chapters bring together a large cross-section of the biodiversity community engaged in these methods, attempts to establish a common language across disciplines for understanding and implementing remote sensing of biodiversity across scales. The first part of the book offers a potential basis for remote detection of biodiversity. An overview of the nature of biodiversity is described, along with ways for determining traits of plant biodiversity through spectral analyses across spatial scales and linking spectral data to the tree of life. The second part details what can be detected spectrally and remotely. Specific instrumentation and technologies are described, as well as the technical challenges of detection and data synthesis, collection and processing. The third part discusses spatial resolution and integration across scales and ends with a vision for developing a global biodiversity monitoring system. Topics include spectral and functional variation across habitats and biomes, biodiversity variables for global scale assessment, and the prospects and pitfalls in remote sensing of biodiversity at the global scale

Freedom to learn for the 21st century (education as if people mattered)

The thesis provides a model for freedom in learning by developing a person-centred approach to education consolidated within a more sociological account of power relations in contemporary Higher Education. The growth and decline of humanistic and person-centred approaches in the face of a globalising and marketized education system are described. A more substantial sociological theory of power and the institutions of power is developed by making connections between the work of Carl Rogers, Martin Heidegger and Paolo Freire. Heidegger's critique of technology is used to reveal deeper structures behind contemporary educational processes which show that education has been increasingly occupied by a technological enframing, by way of assessment and the culture of efficiency, eclipsing models of education which prioritise the person in the process. Rogers’ focus on the person and his individualistic notions of ‘power’ and ‘power over’ are contrasted to Freire’s focus on the community and his Marxian awareness of and resistance to oppressive hierarchy. The theoretical framing for humanistic and empowering learning is supported by virtual, institutional and alternative educational initiatives and a call for a robust and sustainable model of education to empower the person in the process and to let learn