This gem describes a standard method for generating synthetic spatial data that can be used in benchmarking and scalability tests. The goal is to improve the reproducibility and increase the trust in experiments on synthetic data by using standard widely acceptable dataset distributions. In addition, this article describes how to assign a unique identifier to each synthetic dataset that can be shared in papers for reproducibility of results. Finally, this gem provides a supplementary material that gives a reference implementation for all the provided distributions
In this chapter, visualization is used to evaluate the performance of global-scale computational alg...
Spatial data mining is a promising technique that deals with extraction of implicit knowledge or oth...
This paper defines the Field data type for big spatial data. Most big spatial data sets provide info...
An efficient benchmarking environment for spatiotemporal access methods should at least include modu...
Database benchmarking is most valuable if real-life data and workloads are available. However, rea...
The quality of spatial data has a massive impact on its usability. It is therefore critical to both ...
Script to generate synthetic point data based on the spatial distribution characteristics of a pheno...
International audienceThe sensitivity analysis and validation of simulation models require specific ...
Whilst there is an ever increasing amount of openly available spatial-data for teaching and learning...
This dataset contains a set of synthetic data that can be used to evaluate the efficiency of geosapt...
A procedural generation of landscapes often meets a need for real spatial data at finer resolution t...
Reproducibility is one of the corner stones of science: when studies cannot be reproduced it is hard...
Abstract- Effective use of data stored in spatial databases requires methods for evaluation and enha...
The main goal of this paper is to propose a method for storing and manipulating crisp and vague spat...
We give an overview of the papers published in this special issue on spatial statistics, of the Jour...
In this chapter, visualization is used to evaluate the performance of global-scale computational alg...
Spatial data mining is a promising technique that deals with extraction of implicit knowledge or oth...
This paper defines the Field data type for big spatial data. Most big spatial data sets provide info...
An efficient benchmarking environment for spatiotemporal access methods should at least include modu...
Database benchmarking is most valuable if real-life data and workloads are available. However, rea...
The quality of spatial data has a massive impact on its usability. It is therefore critical to both ...
Script to generate synthetic point data based on the spatial distribution characteristics of a pheno...
International audienceThe sensitivity analysis and validation of simulation models require specific ...
Whilst there is an ever increasing amount of openly available spatial-data for teaching and learning...
This dataset contains a set of synthetic data that can be used to evaluate the efficiency of geosapt...
A procedural generation of landscapes often meets a need for real spatial data at finer resolution t...
Reproducibility is one of the corner stones of science: when studies cannot be reproduced it is hard...
Abstract- Effective use of data stored in spatial databases requires methods for evaluation and enha...
The main goal of this paper is to propose a method for storing and manipulating crisp and vague spat...
We give an overview of the papers published in this special issue on spatial statistics, of the Jour...
In this chapter, visualization is used to evaluate the performance of global-scale computational alg...
Spatial data mining is a promising technique that deals with extraction of implicit knowledge or oth...
This paper defines the Field data type for big spatial data. Most big spatial data sets provide info...