# Enhancing DSS Exploitation Based on VGI Quality Assessment: Conceptual Framework and Experimental Evaluation

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## Abstract

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## Featured Application

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## Abstract

## 1. Introduction

## 2. Related Works

## 3. Integrating VGI Data in SDSS

#### 3.1. Conceptual Framework for Integrating VGI Data into Datacubes

#### 3.2. Indicators for Assessing VGI Data Quality

#### 3.2.1. Completeness

_{d}is calculated as follows:

_{td}is calculated according the ratio of the sum of the dimensions’ quality of a given datacube to the number of dimensions nd (as shown in Formula (2))

_{m}is calculated as follows:

_{tm}is calculated according the ratio of the sum of the measures’ quality of a given datacube to the number of measures nm (as shown in Formula (4)).

_{m}= (3/4 × 3/3) = 0.75.

#### 3.2.2. Time Relevance

_{tm}is calculated as follows:

_{m}= 1− (24/36) = 0.33.

#### 3.3. Algorithm for Recommending VGI Data Integration

- -
- Integrate VGI, if VGI data that has high quality with regard to analytical requirements. High-quality VGI data have an overall quality value above a threshold that is defined by users in collaboration with VGI analysts (e.g., an overall quality value greater than or equal to a threshold of 0.75).
- -
- Not to integrate VGI data that does not have high quality (e.g., less than a threshold of 0.75).

Algorithm 1 VGI Quality for Spatial DSS |

1: Input: 2: Nq: number of quality indicators 3: thr: threshold of acceptable data quality 4: Output: Rec: recommendation to the user 5: Begin6: Rec ← “” 7: SQ ← 0 8: For i ← 1 to Nq do9: determine qi 10: determine wi 11: SQ ← SQ + wi ∗ qi 12: End13: Q ← SQ/Nq 14: if (Q ≥ thr) then 15: Rec ← Recommending integrating VGI data into spatial datacube 16: else17: Rec ← Recommending not to integrate VGI data into spatial datacube 18: End |

## 4. The Map-Report Prototype

## 5. Experiments and Results

#### 5.1. Integrating Assessed VGI Data into DSS Datacube

#### 5.1.1. The Spatial Datacube

#### 5.1.2. Integrating VGI into Spatial Datacube

#### 5.2. Experiments and Results

- Having:
- n: the number of queries launched on the DSS; n = 25
- R
_{i}: the real expected value of the result of query i - S
_{i}: the value presenting the response to query i on datacube 1 (the DSS does not take VGI quality assessment into consideration) - S’
_{i}: the value presenting the response to query i on datacube 2 (the DSS integrates VGI quality assessment) - T
_{i}: the absolute error relating to the query i without quality assessment - T’i: the absolute error relating to query i with quality assessment
- T
_{i}and T’i are obtained as follows - $Ti=\left|Si-Ri\right|$
- $T\u2019i=\left|S\u2019i-Ri\right|$
- MAE
_{cube1}: the mean absolute error without VGI quality assessment - MAE
_{cube2}: the mean absolute error with VGI quality assessment - MAE
_{cube1}= $\sum}_{i=1}^{n}Ti/n$ - MAE’
_{cube2}= $\sum}_{i=1}^{n}T\prime i/n$ - MQE
_{cube1:}The mean quadratic error without VGI quality assessment - MQE
_{cube2:}The mean quadratic error with VGI quality assessment - MQE
_{cube1}= $\sum}_{i=0}^{n}(\mathrm{Si}-\mathrm{Ri})\xb2/n$, - MQE
_{cube2}= $\sum}_{i=0}^{n}(\mathrm{S}\u2019\mathrm{i}-\mathrm{Ri})\xb2/n$ - RMAE
_{cube 1}= the root mean squared error without VGI quality assessment - RMAE
_{cube2}= the root mean squared error with VGI quality assessment - RMAE= $\sqrt{{\displaystyle \sum}_{i=0}^{n}(\mathrm{Si}-\mathrm{Ri})\xb2/n}$
- RMAE’= $\sqrt{{\displaystyle \sum}_{i=0}^{n}(\mathrm{S}\u2019\mathrm{i}-\mathrm{Ri})\xb2/n}$

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 2.**Framework for assessing VGI quality and integrating high quality VGI data into DSS datacube.

**Figure 5.**Correlation between quality assessment of Map-Report and quality assessment provided by road maintenance experts.

Datacube 1 | Datacube 2 | |
---|---|---|

MAE | 78.545 | 17.818 |

MQE | 12,085.181 | 663.473 |

RFSE | 109.932 | 25.757 |

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Sboui, T.; Aissi, S.
Enhancing DSS Exploitation Based on VGI Quality Assessment: Conceptual Framework and Experimental Evaluation. *Systems* **2023**, *11*, 393.
https://doi.org/10.3390/systems11080393

**AMA Style**

Sboui T, Aissi S.
Enhancing DSS Exploitation Based on VGI Quality Assessment: Conceptual Framework and Experimental Evaluation. *Systems*. 2023; 11(8):393.
https://doi.org/10.3390/systems11080393

**Chicago/Turabian Style**

Sboui, Tarek, and Saida Aissi.
2023. "Enhancing DSS Exploitation Based on VGI Quality Assessment: Conceptual Framework and Experimental Evaluation" *Systems* 11, no. 8: 393.
https://doi.org/10.3390/systems11080393