Uncertainty Analysis of Rainfall Spatial Interpolation in Urban Small Area

Huang, Jie; Jing, Changfeng; Fu, Jiayun; Huang, Zejun

doi:10.1007/978-3-030-12971-2_5

Cited by 5 publications

(2 citation statements)

References 40 publications

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“…Although the buried method can protect its transform and damage, but its shape and directory cannot inspect easily, therefore, its spatial data quality is crucial for the land management and its operation. As stated in many literature (Huang et al, 2019;Jing et al, 2019;Kilkenny and Robinson, 2018;Najafabadi et al, 2015), "garbage in, garbage out", which is a big challenge for various data. The poor data quality will lead to the bias decision or analysis result.…”

Section: Introductionmentioning

confidence: 99%

A Praxis on Data Quality Evaluation of Underground Pipeline

Zhao

Zhou

Jing

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Underground pipelines are known as “life line”. With the rapid developing of city, more and more pipelines like power lines will move into underground. Facing the complex environment from underground and relationship with other kinds of pipeline, the data quality evaluation is very crucial for academic and business applications. This paper introduced our praxis on underground pipeline data quality on a real project. The datasets are mainly composing of vector data about 15 GB size, covers 3 counties, worked with 3 teams. The workflow, data sampling method and quality evaluation method were engaged in our work. This work can extend to other underground pipeline projects or similar spatial data quality evaluation projects.

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Section: Introductionmentioning

confidence: 99%

A Praxis on Data Quality Evaluation of Underground Pipeline

Zhao

Zhou

Jing

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…Data quality is crucial for the reliability and usage of data, which provides benefit for many application and services such as urban planning, land sources and natural disaster management (Basiri et al, 2019;Birigazzi et al, 2019;Kitchin and McArdle, 2016;Li et al, 2020). As stated in many literature (Huang et al, 2019;Jing et al, 2019;Kilkenny and Robinson, 2018;Najafabadi et al, 2015), "garbage in, garbage out", which is a big challenge for various data. The poor data quality will lead to the bias decision or analysis result.…”

Section: Introductionmentioning

confidence: 99%

A Full Life Data Quality Workflow Research and Project Practice

Zhao

Gao

Jing

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. With the development of technology and geospatial equipment, more and more work, particularly covering a big area survey and mapping work, are conducted with different worker and equipment, or heterogeneous time variation. With the traditional data quality focus on the geospatial data quality itself, which is not include the work files and organization. Facing the heterogeneous characteristics of geospatial data and work organization, a full life data quality workflow was proposed to manage and control geospatial data quality. The proposed workflow is extended to include the file preparation, quality evaluation, and quality calculation phrases. It is demonstrated in one real data quality project, which include vector data, raster data and other geospatial data covering 160 thousands square kilometers and 300 work zones finished by 8 teams. The usability and reliability were validated in our work.

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Response of forest belt on the south slope of Tianshan Mountains in China to global warming during 1990–2020

Zheng,

Zhang,

et al. 2024

Front. Earth Sci.

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Uncertainty Analysis of Rainfall Spatial Interpolation in Urban Small Area

Cited by 5 publications

References 40 publications

A Praxis on Data Quality Evaluation of Underground Pipeline

A Praxis on Data Quality Evaluation of Underground Pipeline

A Full Life Data Quality Workflow Research and Project Practice

Response of forest belt on the south slope of Tianshan Mountains in China to global warming during 1990–2020

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