Spatio-Temporal Visualization Method for Urban Waterlogging
Warning Based on Dynamic Grading

J 2020

Spatio-Temporal Visualization Method for Urban Waterlogging Warning Based on Dynamic Grading

ZHOU, Jingyi, Jie SHEN, Kaiyue ZANG, Xiao SHI, Yixian DU et. al.

Základní údaje

Originální název

Spatio-Temporal Visualization Method for Urban Waterlogging Warning Based on Dynamic Grading

Autoři

ZHOU, Jingyi, Jie SHEN, Kaiyue ZANG, Xiao SHI, Yixian DU a Petr ŠILHÁK (203 Česká republika, domácí)

Vydání

ISPRS International Journal of Geo-Information, Basel, MDPI, 2020, 2220-9964

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10508 Physical geography

Stát vydavatele

Švýcarsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 2.899

Kód RIV

RIV/00216224:14310/20:00117941

Organizační jednotka

Přírodovědecká fakulta

DOI

http://dx.doi.org/10.3390/ijgi9080471

UT WoS

000565184000001

Klíčová slova anglicky

spatio-temporal visualization; urban waterlogging; disaster warning; visual granularity; dynamic grading

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 21. 1. 2021 14:14, Mgr. Marie Šípková, DiS.

Anotace

V originále

With the acceleration of the urbanization process, the problems caused by extreme weather such as heavy rainstorm events have become more and more serious. During such events, the road and its auxiliary facilities may be damaged in the process of the rainstorm and waterlogging, resulting in the decline of its traffic capacity. Rainfall is a continuous process in a space-time dimension, and as rainfall data are obtained through discrete monitoring stations, the acquired rainfall data have discrete characteristics of time interval and space. In order to facilitate users in understanding the impact of urban waterlogging on traffic, the visualization of waterlogging information needs to be displayed under different spatial and temporal granularity. Therefore, the appropriateness of the visualization granularity directly affects the user's cognition of the road waterlogging map. To solve this problem, this paper established a spatial granularity and temporal granularity computing quantitative model for spatio-temporal visualization of road waterlogging and the evaluation method of the model was based on the cognition experiment. The minimum visualization unit of the road section is 50 m and we proposed a 5-level depth grading method and two color schemes for road waterlogging visualization based on the user's cognition. To verify the feasibility of the method, we developed a prototype system and implemented a dynamic spatio-temporal visualization of the waterlogging process in the main urban area of Nanjing, China. The user cognition experiment showed that most participants thought that the segmentation of road was helpful to the local visual expression of waterlogging, and the color schemes of waterlogging depth were also helpful to display the road waterlogging information more effectively.

Citovat

ZHOU, Jingyi, Jie SHEN, Kaiyue ZANG, Xiao SHI, Yixian DU a Petr ŠILHÁK. Spatio-Temporal Visualization Method for Urban Waterlogging Warning Based on Dynamic Grading. ISPRS International Journal of Geo-Information. Basel: MDPI, 2020, roč. 9, č. 8, s. 1-18. ISSN 2220-9964. Dostupné z: https://dx.doi.org/10.3390/ijgi9080471.

@article{1731517,
   author = {Zhou, Jingyi and Shen, Jie and Zang, Kaiyue and Shi, Xiao and Du, Yixian and Šilhák, Petr},
   article_location = {Basel},
   article_number = {8},
   doi = {http://dx.doi.org/10.3390/ijgi9080471},
   keywords = {spatio-temporal visualization; urban waterlogging; disaster warning; visual granularity; dynamic grading},
   language = {eng},
   issn = {2220-9964},
   journal = {ISPRS International Journal of Geo-Information},
   title = {Spatio-Temporal Visualization Method for Urban Waterlogging Warning Based on Dynamic Grading},
   url = {https://doi.org/10.3390/ijgi9080471},
   volume = {9},
   year = {2020}
}

TY  - JOUR
ID  - 1731517
AU  - Zhou, Jingyi - Shen, Jie - Zang, Kaiyue - Shi, Xiao - Du, Yixian - Šilhák, Petr
PY  - 2020
TI  - Spatio-Temporal Visualization Method for Urban Waterlogging Warning Based on Dynamic Grading
JF  - ISPRS International Journal of Geo-Information
VL  - 9
IS  - 8
SP  - 1-18
EP  - 1-18
PB  - MDPI
SN  - 22209964
KW  - spatio-temporal visualization
KW  - urban waterlogging
KW  - disaster warning
KW  - visual granularity
KW  - dynamic grading
UR  - https://doi.org/10.3390/ijgi9080471
L2  - https://doi.org/10.3390/ijgi9080471
N2  - With the acceleration of the urbanization process, the problems caused by extreme weather such as heavy rainstorm events have become more and more serious. During such events, the road and its auxiliary facilities may be damaged in the process of the rainstorm and waterlogging, resulting in the decline of its traffic capacity. Rainfall is a continuous process in a space-time dimension, and as rainfall data are obtained through discrete monitoring stations, the acquired rainfall data have discrete characteristics of time interval and space. In order to facilitate users in understanding the impact of urban waterlogging on traffic, the visualization of waterlogging information needs to be displayed under different spatial and temporal granularity. Therefore, the appropriateness of the visualization granularity directly affects the user's cognition of the road waterlogging map. To solve this problem, this paper established a spatial granularity and temporal granularity computing quantitative model for spatio-temporal visualization of road waterlogging and the evaluation method of the model was based on the cognition experiment. The minimum visualization unit of the road section is 50 m and we proposed a 5-level depth grading method and two color schemes for road waterlogging visualization based on the user's cognition. To verify the feasibility of the method, we developed a prototype system and implemented a dynamic spatio-temporal visualization of the waterlogging process in the main urban area of Nanjing, China. The user cognition experiment showed that most participants thought that the segmentation of road was helpful to the local visual expression of waterlogging, and the color schemes of waterlogging depth were also helpful to display the road waterlogging information more effectively.
ER  -

ZHOU, Jingyi, Jie SHEN, Kaiyue ZANG, Xiao SHI, Yixian DU a Petr ŠILHÁK. Spatio-Temporal Visualization Method for Urban Waterlogging Warning Based on Dynamic Grading. \textit{ISPRS International Journal of Geo-Information}. Basel: MDPI, 2020, roč.~9, č.~8, s.~1-18. ISSN~2220-9964. Dostupné z: https://dx.doi.org/10.3390/ijgi9080471.

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