Land Reuse processes are large planning and decision-making processes based on a large amount of geographic data. Therefore, it is essential that this data is as accurate as possible. However, errors can occur during the creation of the data and not all of them are directly noticeable. We report here what errors we have encountered while working with this geographic data, what problems they can cause, and how we have fixed them. Since the correction can be very time-consuming with the enormous amount of data, we have focused on an automatic correction. Not all of this data can be corrected this way, for the rest, we briefly indicate a procedure to support and simplify the manual correction.
Open pit mines left many regions worldwide inhospitable or uninhabitable. Many sites are left behind in a hazardous or contaminated state, show remnants of waste, or have other restrictions imposed upon them, e.g., for the protection of human or nature. Such information has to be permanently managed in order to reuse those areas in the future. In this work we present and evaluate an automated workflow for supporting the post-mining management of former lignite open pit mines in the eastern part of Germany, where prior to any planned land reuse, aforementioned information has to be acquired to ensure the safety and validity of such an endeavor. Usually, this information is found in expert reports, either in the form of paper documents, or in the best case as digitized unstructured text—all of them in German language. However, due to the size and complexity of these documents, any inquiry is tedious and time-consuming, thereby slowing down or even obstructing the reuse of related areas. Since no training data is available, we employ active learning in order to perform multi-label sentence classification for two categories of restrictions and seven categories of topics. The final system integrates optical character recognition (OCR), active-learning-based text classification, and geographic information system visualization in order to effectively extract, query, and visualize this information for any area of interest. Active learning and text classification results are twofold: Whereas the restriction categories were reasonably accurate (>0.85 F1), the seven topic-oriented categories seemed to be complex even for human annotators and achieved mediocre evaluation scores (<0.70 F1).
In epigenetics, the change of the combination of histone modifications at the same genomic location during cell differentiation is of great interest for understanding the function of these modifications and their combinations. Besides analyzing them locally for individual genomic locations or globally using correlations between different cells types, intermediate level analyses of these changes are of interest. More specifically, the different distributions of these combinations for different cell types, respectively, are compared to gain new insights.
Results and Discussion
We propose a new tool called Masakari that allows segmenting genomes based on lists of ranges having a certain property, e.g., peaks describing histone modifications. It provides a graphical user interface allowing to select all data sets and setting all parameters needed for the segmentation process. Moreover, the graphical user interface provides statistical graphics allowing to assess the quality and suitability of the segmentation and the selected data.
Masakari provides statistics based visualizations and thus fosters insights into the combination of histone modification marks on genome ranges, and the differences of the distribution of these combinations between different cell types.
In mathematics, fractals do not only occur as the product of special curves, but can also represent the result space of typical arithmetic operations. Thus all polynomials with the coefficients -1 and 1 form the following fractal up to a certain degree:
In the generated image the complex roots were calculated for all polynomials with the coefficients -1 and 1 up to degree 40. The real part is used as x-coordinate and the imaginary part as y-coordinate.This results, for example, in various dragon fractals.
Additionally, the frequency of a zero at a coordinate can be coded by the color value, resulting in the following images:
More background information about these fractals can be found at Link.
In the first approach to calculate the roots of all polynomials up to a certain degree, Laguerre's Method (https://mathworld.wolfram.com/LaguerresMethod.html) was used to approximate all complex roots. All roots with no imaginary part were discarded. Laguerre's method works reliably with polynomials with a degree lower than 30. With even larger polynomials the computational time of this method increases very strongly. Therefore, the roots were determined by calculating the eigenvalues of the corresponding matrix (https://en.wikipedia.org/wiki/Characteristic_polynomial). Due to the huge amount of possible polynomials, the calculation was divided into several batches and took place on an HPC cluster. More than 2.8 million CPU hours were spent to calculate the fractal.
To shrink down the amount of necessary storage, the calculated roots were binned into a 10.000 x 6.250 matrix and only the frequency of each root was exported. The exported images have therefore a resolution of 62.5 megapixels.
The data sets are provided as CSV files with x and y coordinates and the frequency. '40-fractal-binned.zip' contains the binned roots normalized to the range 0,10000 on the x-axis and 0,6250 on the y-axis. '40-fractal-coordinates.zip' contains the denormalized roots in the range -4,4 on the x-axis and -2.5,2.5 on the y-axis. As far as known there is no data set with a higher degree.
The data sets are licensed under the Creative Commons Attribution 4.0 International License.
Municipal authorities have, among other tasks, a great interest in supporting their local economy. For this purpose, they provide consulting offices that advise companies and mediate cooperation partners.
The city administration of Leipzig created a business register in which companies can provide their competences in free text fields. This business register contains over 1000 entries and it is not straight forward to find and compare companies based on their self-descriptions.
In this paper, we propose a new visualization to analyze the distribution of local companies and exploring the competence profiles of the companies. In order to visualize connections between companies, we perform a semantical analysis. In detail, we use the management staff listing and the core competence descriptions to link the entries. The company location and the connections between the companies are visualized on a map or as a graph. The visualization provides several filtering and interaction mechanisms on demand. From a governance perspective, this leads to insights into company and industry sector networks within a local economic zone.