scholarly journals Semantics of Voids within Data: Ignorance-Aware Machine Learning

2021 ◽  
Vol 10 (4) ◽  
pp. 246
Author(s):  
Vagan Terziyan ◽  
Anton Nikulin
Keyword(s):  
Spatial Data ◽  
Information Science ◽  
Nearest Neighbor ◽  
Geographical Information ◽  
Two Dimensional ◽  
Prototype Selection ◽  
Data Space ◽  
Important Concern ◽  
Traditional Classification ◽  
Nearest Neighbor Classifiers

Operating with ignorance is an important concern of geographical information science when the objective is to discover knowledge from the imperfect spatial data. Data mining (driven by knowledge discovery tools) is about processing available (observed, known, and understood) samples of data aiming to build a model (e.g., a classifier) to handle data samples that are not yet observed, known, or understood. These tools traditionally take semantically labeled samples of the available data (known facts) as an input for learning. We want to challenge the indispensability of this approach, and we suggest considering the things the other way around. What if the task would be as follows: how to build a model based on the semantics of our ignorance, i.e., by processing the shape of “voids” within the available data space? Can we improve traditional classification by also modeling the ignorance? In this paper, we provide some algorithms for the discovery and visualization of the ignorance zones in two-dimensional data spaces and design two ignorance-aware smart prototype selection techniques (incremental and adversarial) to improve the performance of the nearest neighbor classifiers. We present experiments with artificial and real datasets to test the concept of the usefulness of ignorance semantics discovery.

scholarly journals Grounded Design and GIScience - A framework for informing the design of geographical information systems and spatial data infrastructures

2019 ◽  
Author(s):  
Alexander Kmoch ◽  
Evelyn Uuemaa ◽  
Hermann Klug
Keyword(s):  
Information Systems ◽  
Spatial Data ◽  
Geographical Information Systems ◽  
Information Science ◽  
Design Science ◽  
Environmental Data ◽  
Geographical Information ◽  
Data Infrastructure ◽  
Development Framework ◽  
Grounded Design

Geographical Information Science (GIScience), also Geographical Information Science and Systems, is a multi-faceted research discipline and comprises a wide variety of topics. Investigation into data management and interoperability of geographical data and environmental data sets for scientific analysis, visualisation and modelling is an important driver of the Information Science aspect of GIScience, that underpins comprehensive Geographical Information Systems (GIS) and Spatial Data Infrastructure (SDI) research and development. In this article we present the 'Grounded Design' method, a fusion of Design Science Research (DSR) and Grounded Theory (GT), and how they can act as guiding principles to link GIScience, Computer Science and Earth Sciences into a converging GI systems development framework. We explain how this bottom-up research framework can yield holistic and integrated perspectives when designing GIS and SDI systems and software. This would allow GIScience academics, GIS and SDI practitioners alike to reliably draw from interdisciplinary knowledge to consistently design and innovate GI systems.

scholarly journals GEOGRAPHICAL INFORMATION SYSTEM (GISy) IMPLEMENTATION WITHOUT GEOGRAPHICAL INFORMATION SCIENCE (GISc) FUNDAMENTAL

2021 ◽  
Vol 10 (2) ◽  
pp. 807-815
Keyword(s):  
Information System ◽  
Geographical Information System ◽  
Spatial Data ◽  
Professional Training ◽  
Information Science ◽  
Rapid Development ◽  
Geospatial Data ◽  
Geographical Information ◽  
Basic Knowledge ◽  
The Impact

This article reviews the use of Geographical Information System (GIS) has been primarily applied in spatial decision making from simple to complex geospatial problems. GIS is usually referred to as a computer system used explicitly to store, manage, analyze, manipulate, and visualize geospatial data. GIS can produce meaningful information for a better understanding of solving related geographic/spatial problems. With the technology, hardware, and software assistance, GIS is at its progressive pace even though GIS starts with a simple and straightforward question of geographic features/event location. This rapid development has made GIS and spatial data becoming a critical commodity today. However, without the basic knowledge and GIS understanding, the actual GIS capabilities, such as understanding geographical concepts, managing geographic phenomena, and solving geographical problems, become limited. To become worse, GIS is was seen as a tool to facilitate map display and simple spatial analysis. Furthermore, the market's professional training emphasizes simple GIS components such as hardware, software, geospatial data mapping, extracting geographical data from tables (tabular data), simple queries or display, and spatial data editing mastered using GIS manuals in training. Thus, this article highlights the impact of implementing GIS without sufficient GIS fundamental knowledge, resulting in complicated spatial decision planning issues.

COEVOLUTION OF NEAREST NEIGHBOR CLASSIFIERS

2007 ◽  
Vol 21 (05) ◽  
pp. 921-946 ◽  
Author(s):  
CHRISTIAN GAGNÉ ◽  
MARC PARIZEAU
Keyword(s):  
Nearest Neighbor ◽  
Real Data ◽  
Data Sets ◽  
Data Normalization ◽  
Prototype Selection ◽  
Selection Methods ◽  
Proximity Measure ◽  
Classifier Design ◽  
Nearest Neighbor Classifiers ◽  
Better Than

This paper presents experiments of Nearest Neighbor (NN) classifier design using different evolutionary computation methods. Through multiobjective and coevolution techniques, it combines genetic algorithms and genetic programming to both select NN prototypes and design a neighborhood proximity measure, in order to produce a more efficient and robust classifier. The proposed approach is compared with the standard NN classifier, with and without the use of classic prototype selection methods, and classic data normalization. Results on both synthetic and real data sets show that the proposed methodology performs as well or better than other methods on all tested data sets.

scholarly journals Grounded Design and GIScience - A framework for informing the design of geographical information systems and spatial data infrastructures

2019 ◽  
Author(s):  
Alexander Kmoch ◽  
Evelyn Uuemaa ◽  
Hermann Klug
Keyword(s):  
Information Systems ◽  
Spatial Data ◽  
Geographical Information Systems ◽  
Information Science ◽  
Design Science ◽  
Environmental Data ◽  
Geographical Information ◽  
Data Infrastructure ◽  
Development Framework ◽  
Grounded Design

Geographical Information Science (GIScience), also Geographical Information Science and Systems, is a multi-faceted research discipline and comprises a wide variety of topics. Investigation into data management and interoperability of geographical data and environmental data sets for scientific analysis, visualisation and modelling is an important driver of the Information Science aspect of GIScience, that underpins comprehensive Geographical Information Systems (GIS) and Spatial Data Infrastructure (SDI) research and development. In this article we present the 'Grounded Design' method, a fusion of Design Science Research (DSR) and Grounded Theory (GT), and how they can act as guiding principles to link GIScience, Computer Science and Earth Sciences into a converging GI systems development framework. We explain how this bottom-up research framework can yield holistic and integrated perspectives when designing GIS and SDI systems and software. This would allow GIScience academics, GIS and SDI practitioners alike to reliably draw from interdisciplinary knowledge to consistently design and innovate GI systems.

scholarly journals Three-Dimensional Thematic Map Imaging of the Yacht Port on the Example of the Polish National Sailing Centre Marina in Gdańsk

2021 ◽  
Vol 11 (15) ◽  
pp. 7016
Author(s):  
Pawel S. Dabrowski ◽  
Cezary Specht ◽  
Mariusz Specht ◽  
Artur Makar
Keyword(s):  
Spatial Data ◽  
Convex Surface ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Future Research ◽  
Two Dimensional ◽  
Thematic Maps ◽  
Research Directions ◽  
Future Research Directions ◽  
The Many

The theory of cartographic projections is a tool which can present the convex surface of the Earth on the plane. Of the many types of maps, thematic maps perform an important function due to the wide possibilities of adapting their content to current needs. The limitation of classic maps is their two-dimensional nature. In the era of rapidly growing methods of mass acquisition of spatial data, the use of flat images is often not enough to reveal the level of complexity of certain objects. In this case, it is necessary to use visualization in three-dimensional space. The motivation to conduct the study was the use of cartographic projections methods, spatial transformations, and the possibilities offered by thematic maps to create thematic three-dimensional map imaging (T3DMI). The authors presented a practical verification of the adopted methodology to create a T3DMI visualization of the marina of the National Sailing Centre of the Gdańsk University of Physical Education and Sport (Poland). The profiled characteristics of the object were used to emphasize the key elements of its function. The results confirmed the increase in the interpretative capabilities of the T3DMI method, relative to classic two-dimensional maps. Additionally, the study suggested future research directions of the presented solution.

Temporal and Spatial Pattern of a Privet (Ligustrum vulgare) Invasion

2013 ◽  
Vol 6 (2) ◽  
pp. 310-319 ◽  
Author(s):  
Wanying Zhao ◽  
Charles Goebel ◽  
John Cardina
Keyword(s):  
Spatial Pattern ◽  
Spatial Data ◽  
Nearest Neighbor ◽  
Management Strategies ◽  
Age Distribution ◽  
Lag Phase ◽  
Suitable Habitat ◽  
Second Phase ◽  
Invasion History ◽  
Cumulative Number

AbstractPrivet has escaped from cultivation and is invading natural areas throughout eastern North America. Understanding the pattern of invasion over time could help us develop more efficient management strategies. We studied the invasion history and spatial distribution pattern of privet by mapping age and spatial data for established patches in a 132-ha (326 ac) forested natural area in northeast Ohio. We determined the age of 331 geo-referenced patches by counting annual rings, and mapped them with corresponding land habitat. Age distribution and cumulative number of privet patches over about 40 yr showed three phases of invasion. The initial 19-yr lag phase was characterized as a dispersed spatial pattern (based on nearest neighbor analysis), with patches located mostly at edges of different habitats and open places. In a second phase of about 15 yr, an average of 19 patches were initiated yearly, in a pattern that trended towards clustered. The final phase began around 2007, as the rate of new patch establishment declined, possibly because of saturation of the suitable habitat. Establishment of new patches was not associated with specific habitats. Aggregation of patches with similar ages increased after 1998 and became significantly clustered. Mapping of clusters of old and young patches identified invasion hot spots and barriers. Results affirmed that the best time for invasive control is during the lag phase. By monitoring edge habitats associated with early establishment, managers might detect and control early invaders and delay the onset of the expansion phase.

Spatial clustering analysis of green economy based on knowledge graph

2021 ◽  
pp. 1-10
Author(s):  
Shiyuan Zhou ◽  
Xiaoqin Yang ◽  
Qianli Chang
Keyword(s):  
Spatial Data ◽  
Clustering Analysis ◽  
Spatial Clustering ◽  
Evaluation Model ◽  
Principal Component ◽  
Green Economy ◽  
Membership Degree ◽  
Two Dimensional ◽  
Dimensional Graph ◽  
Improved Algorithm

By organically combining principal component analysis, spatial autocorrelation algorithm and two-dimensional graph theory clustering algorithm, the comprehensive evaluation model of regional green economy is explored and established. Based on the evaluation index system of regional green economy, this paper evaluates the development of regional green economy comprehensively by using principal component analysis, and evaluates the competitive advantage of green economy and analyzes the spatial autocorrelation based on the evaluation results. Finally, the green economy and local index score as observed values, by using the method of two-dimensional graph clustering analysis of spatial clustering. In view of the fuzzy k –modes cluster membership degree measure method without considering the defects of the spatial distribution of object, double the distance and density measurement of measure method is introduced into the fuzzy algorithm of k –modes, thus in a more reasonable way to update the membership degree of the object. Vote, MUSH-ROOM and ZOO data sets in UCI machine learning library were used for testing, and the F value of the improved algorithm was better than that of the previous one, indicating that the improved algorithm had good clustering effect. Finally, the improved algorithm is applied to the spatial data collected from Baidu Map to cluster, and a good clustering result is obtained, which shows the feasibility and effectiveness of the algorithm applied to spatial data. Results show that the development of green economy using the analysis method of combining quantitative analysis and qualitative analysis, explores the connotation of green economy with space evaluation model is feasible, small make up for the qualitative analysis of the green economy in the past, can objective system to reflect the regional green economic development level, will help policy makers scientific formulating regional economic development strategy, green integrated development of regional green economy from the macroscopic Angle, the development of network system.

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