# Assessing Essential Qualities of Urban Space with Emotional and Visual Data Based on GIS Technique

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## Abstract

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## 1. Introduction

## 2. State of the Art

## 3. Emotion Data Collection

#### 3.1. Physiological Basis

#### 3.2. Preparation of Experiment

#### 3.3. Emotion Data Preprocessing

_{ij}is the spatial weight between i and j in all n objects; and x

_{i}and x

_{j}characterize the magnitude of events i and j.

## 4. Spatial Analysis

#### 4.1. Influence from Building Texture

_{i}refers to the areas of all the architectural outlines within the isovist, $\overline{S}$ refers to the average area of an architectural outline, N refers to the number of architectures, and P refers to the overall length of an architectural outline.

#### 4.2. Isovist Analysis

_{f}refers to the overall lengths of solid boundaries within the isovist area.

_{i}refers to the estimated coefficient of the variable.

_{e}refers to sensitivity, and S

_{p}refers to specificity.

#### 4.3. Analysis of Visual Entropy and Fractals

_{i}refers to the probability that every gray-scale pixel value appears. To eliminate noise, the threshold value was set to 3%. Signals less than the threshold value are considered not to be valid data. Only those regions where the quantity of the pixels is larger than the threshold value in the image are evaluated. To simplify the calculations, this paper divides the image’s gray-scale into 25 grades. The luminance information of the green wave band is quite sufficient as it possesses better image contrast [53]. Consequently, the gray-scale map of this band is considered in this analysis.

_{i}refers to the comprehensive visual index of the sampling site. The numbers 1 and 0 are used to represent the positive and negative symbols of the calculation, matching all the sampling locations’ variability indices with the valence.

## 5. Discussion and Conclusion

- People’s emotions are affected by different building layouts—in particular, how people perceive the spaces between buildings. Among those factors, isovist scope and relevant attributes are important ways for people to obtain visual information during their urban experience. Pedestrians activities in urban spaces are not simply restricted to any single isovist parameter but to the comprehensive impact of several isovist parameters, of which compactness, occlusivity, and maximum visibility are comparatively dominant. Among the three, higher compactness and greater visibility within a space seem to be advantageous in causing positive emotions, indicating that people may prefer spaces with good vistas within a suitable distance and clearer boundaries. However, this does not mean that people prefer an unlimited field of view. Large unending avenues might be monotonous and boring. A threshold effect may occur, and that is the question our future research will seek to answer.
- Spatial attributes are not merely reflected in planar isovist form; the richness and complexity of three-dimensional space are also important reasons affecting the spatial experience of pedestrians. Visual information analysis can help designers effectively interpret the qualities of an urban space. According to this research, enclosed urban spaces are very important in fostering a sense of security in pedestrians. During the process of urban planning and design, specific entity borders, neat and compact isovist forms, a rich landscape hierarchy and greenery are easy ways to create urban spaces with a sense of place. Some man-made obstacles can seriously weaken the qualities of the spatial environment. Only by strengthening management and daily maintenance is it possible to ensure the design achievements, which are hard to obtain, and maintain a spatial environment with positive qualities.
- Human perception of urban space tends to focus on important spatial nodes; therefore, we cannot neglect changes in the spatial sequence or the design treatment of spatial nodes. These should strengthen the systematic construction of urban spatial nodes, including public squares, street greening, and street corners. The integration of points, lines and networks—especially those that reinforce the continuity and network of pedestrian space—should give full weight to the way in which the scenes of these spatial nodes switch and cultivate urban spatial sequences with special meanings that reinforce positive images during urban movement.

## Acknowledgment

## Author Contributions

## Conflicts of Interest

## References

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**Figure 2.**A model for secondary emotions determined by level of arousal (bottom to top denote, respectively, mild to intense) and their valence (left to right denote, respectively, unpleasant to pleasant).

**Figure 3.**Hot-spot emotion clusters. Locations of emotional arousal are color-coded based on z-scores. Similar high or low values are shown in red or blue (

**a**) for positive emotions; and (

**b**) for negative emotions.

**Figure 4.**Shape of isovists at sampling points: (

**a**) for positive emotions; (

**b**) for negative emotions.

**Figure 5.**Comparison among the shape indicators of building texture (

**a**) mean area; (

**b**) area dispersion; (

**c**) average distance between buildings; (

**d**) degree of fragmentation.

**Figure 6.**ROC Analysis: (

**a**) isovist area; (

**b**) isovist perimeter; (

**c**) isovist compactness; (

**d**) neighborhood degree; (

**e**) maximum visibility; (

**f**) minimum visibility; (

**g**) all parameters.

Sampling Group | Mean Area | Area Dispersion | Average Distance | Degree of Fragmentation |
---|---|---|---|---|

P collection (S1) | 1.728 | 1.356 | 1.283 | 0.984 |

N collection (S1) | 1.478 | 1.443 | 0.955 | 0.958 |

P collection (S2) | 0.378 | 1.220 | 0.834 | 1.016 |

N collection (S2) | 0.366 | 1.076 | 0.863 | 1.038 |

Inspection Coefficient | Inspection Result | Prediction Coefficient | Estimated Coefficient | p-Value | Wals Value |
---|---|---|---|---|---|

Cox & Snell R2 | 0.302 | X1 | 1.03 | 0.000 | 36.549 |

Nagelkerke R2 | 0.438 | X2 | 0.10 | 0.032 | 4.611 |

Hosmer-Lemeshow | 0.128 | X3 | 0.70 | 0.000 | 14.709 |

Location No. | Fractal | Visual Entropy | Comprehensive Visual Index | Predicted Value | Observed Value | Inspection Result |
---|---|---|---|---|---|---|

1 | 1.6616 | 2.677428 | 4.339028 | 1 | 1 | √ |

2 | 1.7354 | 2.958627 | 4.694027 | 1 | 1 | √ |

3 | 1.7770 | 2.798628 | 4.575628 | 0 | 0 | √ |

4 | 1.5876 | 2.744836 | 4.332436 | 0 | 0 | √ |

5 | 1.4898 | 2.674102 | 4.163902 | 0 | 1 | × |

6 | 1.6030 | 2.805265 | 4.408265 | 1 | 0 | × |

7 | 1.8466 | 2.999055 | 4.845655 | 1 | 1 | √ |

8 | 1.6561 | 2.979993 | 4.636093 | 0 | 0 | √ |

9 | 1.7202 | 2.915624 | 4.635824 | 0 | 0 | √ |

10 | 1.7974 | 2.998140 | 4.79554 | 1 | 0 | × |

11 | 1.8463 | 3.027640 | 4.87394 | 1 | 1 | √ |

12 | 1.7044 | 3.061204 | 4.765604 | 0 | 1 | × |

13 | 1.6973 | 2.969658 | 4.666958 | 0 | 0 | √ |

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**MDPI and ACS Style**

Li, X.; Hijazi, I.; Koenig, R.; Lv, Z.; Zhong, C.; Schmitt, G.
Assessing Essential Qualities of Urban Space with Emotional and Visual Data Based on GIS Technique. *ISPRS Int. J. Geo-Inf.* **2016**, *5*, 218.
https://doi.org/10.3390/ijgi5110218

**AMA Style**

Li X, Hijazi I, Koenig R, Lv Z, Zhong C, Schmitt G.
Assessing Essential Qualities of Urban Space with Emotional and Visual Data Based on GIS Technique. *ISPRS International Journal of Geo-Information*. 2016; 5(11):218.
https://doi.org/10.3390/ijgi5110218

**Chicago/Turabian Style**

Li, Xin, Ihab Hijazi, Reinhard Koenig, Zhihan Lv, Chen Zhong, and Gerhard Schmitt.
2016. "Assessing Essential Qualities of Urban Space with Emotional and Visual Data Based on GIS Technique" *ISPRS International Journal of Geo-Information* 5, no. 11: 218.
https://doi.org/10.3390/ijgi5110218