A Fast Line Density Visualization Plugin for Geographic Information Systems

Tsz Nam Chan; Bojian Zhu; Dingming Wu; Yun Peng; Leong Hou U; Wei Tu; Ruisheng Wang

doi:10.1145/3722212.3725075

A Fast Line Density Visualization Plugin for Geographic Information Systems

Tsz Nam Chan, Bojian Zhu, Dingming Wu, Yun Peng, Leong Hou U, Wei Tu, Ruisheng Wang

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

Abstract

Line Density Visualization (LDV) has been widely used in different domains, e.g., transportation science, urban planning, and criminology. Therefore, various geographic information systems, e.g., ArcGIS and QGIS, can also support this tool. However, all these GIS platforms mainly adopt the naïve algorithm for generating LDV, which cannot be scalable to high resolution sizes and large-scale line-segment datasets. To tackle this issue, we have developed a new QGIS plugin, called Fast Line Density Analysis, for efficiently supporting two types of accurate approximation, namely (1) generating LDV with an ε-relative error guarantee (εLDV) and (2) generating LDV with multiple thresholds (τLDV). In this demonstration, we have prepared three large-scale datasets (with up to 15.7 million line segments) for participants to compare our plugin with QGIS and ArcGIS in terms of accuracy and efficiency. In particular, participants can also tune different parameters in our plugin for understanding how they can affect the visualization quality and efficiency. The demonstration video is available in the YouTube and Bilibili links, which are https://www.youtube.com/watch?v=EYl3Wieb-2I and https://www.bilibili.com/video/BV1aJwaeMEHr, respectively.

Original language	English
Title of host publication	SIGMOD/PODS '25
Subtitle of host publication	Companion of the 2025 International Conference on Management of Data
Editors	Amol Deshpande, Ashraf Aboulnaga, Babak Salimi, Badrish Chandramouli, Bill Howe, Boon Thau Loo, Boris Glavic, Carlo Curino, Daisy Zhe Wang, Dan Suciu, Daniel Abadi, Divesh Srivastava, Eugene Wu, Faisal Nawab, Ihab Ilyas, Jeffrey Naughton, Jennie Rogers, Jignesh Patel, Joy Arulraj, Jun Yang, Karima Echihabi, Kenneth Ross, Khuzaima Daudjee, Laks Lakshmanan, Minos Garofalakis, Mirek Riedewald, Mohamed Mokbel, Mourad Ouzzani, Oliver Kennedy, Oliver Kennedy, Paolo Papotti, Peter Alvaro, Peter Bailis, Renee Miller, Senjuti Basu Roy, Sergey Melnik, Stratos Idreos, Sudeepa Roy, Theodoros Rekatsinas, Viktor Leis, Wenchao Zhou, Wolfgang Gatterbauer, Zack Ives
Place of Publication	New York
Publisher	Association for Computing Machinery (ACM)
Pages	63–66
Number of pages	4
ISBN (Electronic)	9798400715648
ISBN (Print)	9798400715648
DOIs	https://doi.org/10.1145/3722212.3725075
Publication status	Published - 22 Jun 2025
Event	ACM SIGMOD/PODS International Conference on Management of Data, SIGMOD/PODS 2025 - Intercontinental Berlin, Berlin, Germany Duration: 22 Jun 2025 → 27 Jun 2025 https://2025.sigmod.org/ (Conference website) https://2025.sigmod.org/program_at_a_glance.shtml (Conference program) https://2025.sigmod.org/sigmod_papers.shtml (Accepted papers) https://dl.acm.org/doi/proceedings/10.1145/3722212 (Conference proceeding)

Publication series

Name	Companion of the International Conference on Management of Data
Publisher	Association for Computing Machinery

Conference

Conference	ACM SIGMOD/PODS International Conference on Management of Data, SIGMOD/PODS 2025
Abbreviated title	SIGMOD/PODS 2025
Country/Territory	Germany
City	Berlin
Period	22/06/25 → 27/06/25
Internet address	https://2025.sigmod.org/ (Conference website) https://2025.sigmod.org/program_at_a_glance.shtml (Conference program) https://2025.sigmod.org/sigmod_papers.shtml (Accepted papers) https://dl.acm.org/doi/proceedings/10.1145/3722212 (Conference proceeding)

User-Defined Keywords

GIS
fast plugin
line density visualization

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1145/3722212.3725075

Cite this

Chan, T. N., Zhu, B., Wu, D., Peng, Y., U, L. H., Tu, W., & Wang, R. (2025). A Fast Line Density Visualization Plugin for Geographic Information Systems. In A. Deshpande, A. Aboulnaga, B. Salimi, B. Chandramouli, B. Howe, B. T. Loo, B. Glavic, C. Curino, D. Zhe Wang, D. Suciu, D. Abadi, D. Srivastava, E. Wu, F. Nawab, I. Ilyas, J. Naughton, J. Rogers, J. Patel, J. Arulraj, J. Yang, K. Echihabi, K. Ross, K. Daudjee, L. Lakshmanan, M. Garofalakis, M. Riedewald, M. Mokbel, M. Ouzzani, O. Kennedy, O. Kennedy, P. Papotti, P. Alvaro, P. Bailis, R. Miller, S. B. Roy, S. Melnik, S. Idreos, S. Roy, T. Rekatsinas, V. Leis, W. Zhou, W. Gatterbauer, ... Z. Ives (Eds.), SIGMOD/PODS '25: Companion of the 2025 International Conference on Management of Data (pp. 63–66). (Companion of the International Conference on Management of Data). Association for Computing Machinery (ACM). https://doi.org/10.1145/3722212.3725075

Chan, Tsz Nam ; Zhu, Bojian ; Wu, Dingming et al. / A Fast Line Density Visualization Plugin for Geographic Information Systems. SIGMOD/PODS '25: Companion of the 2025 International Conference on Management of Data. editor / Amol Deshpande ; Ashraf Aboulnaga ; Babak Salimi ; Badrish Chandramouli ; Bill Howe ; Boon Thau Loo ; Boris Glavic ; Carlo Curino ; Daisy Zhe Wang ; Dan Suciu ; Daniel Abadi ; Divesh Srivastava ; Eugene Wu ; Faisal Nawab ; Ihab Ilyas ; Jeffrey Naughton ; Jennie Rogers ; Jignesh Patel ; Joy Arulraj ; Jun Yang ; Karima Echihabi ; Kenneth Ross ; Khuzaima Daudjee ; Laks Lakshmanan ; Minos Garofalakis ; Mirek Riedewald ; Mohamed Mokbel ; Mourad Ouzzani ; Oliver Kennedy ; Oliver Kennedy ; Paolo Papotti ; Peter Alvaro ; Peter Bailis ; Renee Miller ; Senjuti Basu Roy ; Sergey Melnik ; Stratos Idreos ; Sudeepa Roy ; Theodoros Rekatsinas ; Viktor Leis ; Wenchao Zhou ; Wolfgang Gatterbauer ; Zack Ives. New York : Association for Computing Machinery (ACM), 2025. pp. 63–66 (Companion of the International Conference on Management of Data).

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title = "A Fast Line Density Visualization Plugin for Geographic Information Systems",

abstract = "Line Density Visualization (LDV) has been widely used in different domains, e.g., transportation science, urban planning, and criminology. Therefore, various geographic information systems, e.g., ArcGIS and QGIS, can also support this tool. However, all these GIS platforms mainly adopt the na{\"i}ve algorithm for generating LDV, which cannot be scalable to high resolution sizes and large-scale line-segment datasets. To tackle this issue, we have developed a new QGIS plugin, called Fast Line Density Analysis, for efficiently supporting two types of accurate approximation, namely (1) generating LDV with an ε-relative error guarantee (εLDV) and (2) generating LDV with multiple thresholds (τLDV). In this demonstration, we have prepared three large-scale datasets (with up to 15.7 million line segments) for participants to compare our plugin with QGIS and ArcGIS in terms of accuracy and efficiency. In particular, participants can also tune different parameters in our plugin for understanding how they can affect the visualization quality and efficiency. The demonstration video is available in the YouTube and Bilibili links, which are https://www.youtube.com/watch?v=EYl3Wieb-2I and https://www.bilibili.com/video/BV1aJwaeMEHr, respectively.",

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author = "Chan, {Tsz Nam} and Bojian Zhu and Dingming Wu and Yun Peng and U, {Leong Hou} and Wei Tu and Ruisheng Wang",

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booktitle = "SIGMOD/PODS '25",

address = "United States",

url = "https://2025.sigmod.org/, https://2025.sigmod.org/program_at_a_glance.shtml, https://2025.sigmod.org/sigmod_papers.shtml, https://dl.acm.org/doi/proceedings/10.1145/3722212",

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Chan, TN, Zhu, B, Wu, D, Peng, Y, U, LH, Tu, W & Wang, R 2025, A Fast Line Density Visualization Plugin for Geographic Information Systems. in A Deshpande, A Aboulnaga, B Salimi, B Chandramouli, B Howe, BT Loo, B Glavic, C Curino, D Zhe Wang, D Suciu, D Abadi, D Srivastava, E Wu, F Nawab, I Ilyas, J Naughton, J Rogers, J Patel, J Arulraj, J Yang, K Echihabi, K Ross, K Daudjee, L Lakshmanan, M Garofalakis, M Riedewald, M Mokbel, M Ouzzani, O Kennedy, O Kennedy, P Papotti, P Alvaro, P Bailis, R Miller, SB Roy, S Melnik, S Idreos, S Roy, T Rekatsinas, V Leis, W Zhou, W Gatterbauer & Z Ives (eds), SIGMOD/PODS '25: Companion of the 2025 International Conference on Management of Data. Companion of the International Conference on Management of Data, Association for Computing Machinery (ACM), New York, pp. 63–66, ACM SIGMOD/PODS International Conference on Management of Data, SIGMOD/PODS 2025, Berlin, Germany, 22/06/25. https://doi.org/10.1145/3722212.3725075

A Fast Line Density Visualization Plugin for Geographic Information Systems. / Chan, Tsz Nam; Zhu, Bojian; Wu, Dingming et al.
SIGMOD/PODS '25: Companion of the 2025 International Conference on Management of Data. ed. / Amol Deshpande; Ashraf Aboulnaga; Babak Salimi; Badrish Chandramouli; Bill Howe; Boon Thau Loo; Boris Glavic; Carlo Curino; Daisy Zhe Wang; Dan Suciu; Daniel Abadi; Divesh Srivastava; Eugene Wu; Faisal Nawab; Ihab Ilyas; Jeffrey Naughton; Jennie Rogers; Jignesh Patel; Joy Arulraj; Jun Yang; Karima Echihabi; Kenneth Ross; Khuzaima Daudjee; Laks Lakshmanan; Minos Garofalakis; Mirek Riedewald; Mohamed Mokbel; Mourad Ouzzani; Oliver Kennedy; Oliver Kennedy; Paolo Papotti; Peter Alvaro; Peter Bailis; Renee Miller; Senjuti Basu Roy; Sergey Melnik; Stratos Idreos; Sudeepa Roy; Theodoros Rekatsinas; Viktor Leis; Wenchao Zhou; Wolfgang Gatterbauer; Zack Ives. New York: Association for Computing Machinery (ACM), 2025. p. 63–66 (Companion of the International Conference on Management of Data).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - A Fast Line Density Visualization Plugin for Geographic Information Systems

AU - Chan, Tsz Nam

AU - Zhu, Bojian

AU - Wu, Dingming

AU - Peng, Yun

AU - U, Leong Hou

AU - Tu, Wei

AU - Wang, Ruisheng

PY - 2025/6/22

Y1 - 2025/6/22

N2 - Line Density Visualization (LDV) has been widely used in different domains, e.g., transportation science, urban planning, and criminology. Therefore, various geographic information systems, e.g., ArcGIS and QGIS, can also support this tool. However, all these GIS platforms mainly adopt the naïve algorithm for generating LDV, which cannot be scalable to high resolution sizes and large-scale line-segment datasets. To tackle this issue, we have developed a new QGIS plugin, called Fast Line Density Analysis, for efficiently supporting two types of accurate approximation, namely (1) generating LDV with an ε-relative error guarantee (εLDV) and (2) generating LDV with multiple thresholds (τLDV). In this demonstration, we have prepared three large-scale datasets (with up to 15.7 million line segments) for participants to compare our plugin with QGIS and ArcGIS in terms of accuracy and efficiency. In particular, participants can also tune different parameters in our plugin for understanding how they can affect the visualization quality and efficiency. The demonstration video is available in the YouTube and Bilibili links, which are https://www.youtube.com/watch?v=EYl3Wieb-2I and https://www.bilibili.com/video/BV1aJwaeMEHr, respectively.

AB - Line Density Visualization (LDV) has been widely used in different domains, e.g., transportation science, urban planning, and criminology. Therefore, various geographic information systems, e.g., ArcGIS and QGIS, can also support this tool. However, all these GIS platforms mainly adopt the naïve algorithm for generating LDV, which cannot be scalable to high resolution sizes and large-scale line-segment datasets. To tackle this issue, we have developed a new QGIS plugin, called Fast Line Density Analysis, for efficiently supporting two types of accurate approximation, namely (1) generating LDV with an ε-relative error guarantee (εLDV) and (2) generating LDV with multiple thresholds (τLDV). In this demonstration, we have prepared three large-scale datasets (with up to 15.7 million line segments) for participants to compare our plugin with QGIS and ArcGIS in terms of accuracy and efficiency. In particular, participants can also tune different parameters in our plugin for understanding how they can affect the visualization quality and efficiency. The demonstration video is available in the YouTube and Bilibili links, which are https://www.youtube.com/watch?v=EYl3Wieb-2I and https://www.bilibili.com/video/BV1aJwaeMEHr, respectively.

KW - GIS

KW - fast plugin

KW - line density visualization

UR - https://dl.acm.org/doi/10.1145/3722212.3725075

UR - http://www.scopus.com/inward/record.url?scp=105010220380&partnerID=8YFLogxK

U2 - 10.1145/3722212.3725075

DO - 10.1145/3722212.3725075

M3 - Conference proceeding

SN - 9798400715648

T3 - Companion of the International Conference on Management of Data

SP - 63

EP - 66

BT - SIGMOD/PODS '25

A2 - Deshpande, Amol

A2 - Aboulnaga, Ashraf

A2 - Salimi, Babak

A2 - Chandramouli, Badrish

A2 - Howe, Bill

A2 - Loo, Boon Thau

A2 - Glavic, Boris

A2 - Curino, Carlo

A2 - Zhe Wang, Daisy

A2 - Suciu, Dan

A2 - Abadi, Daniel

A2 - Srivastava, Divesh

A2 - Wu, Eugene

A2 - Nawab, Faisal

A2 - Ilyas, Ihab

A2 - Naughton, Jeffrey

A2 - Rogers, Jennie

A2 - Patel, Jignesh

A2 - Arulraj, Joy

A2 - Yang, Jun

A2 - Echihabi, Karima

A2 - Ross, Kenneth

A2 - Daudjee, Khuzaima

A2 - Lakshmanan, Laks

A2 - Garofalakis, Minos

A2 - Riedewald, Mirek

A2 - Mokbel, Mohamed

A2 - Ouzzani, Mourad

A2 - Kennedy, Oliver

A2 - Papotti, Paolo

A2 - Alvaro, Peter

A2 - Bailis, Peter

A2 - Miller, Renee

A2 - Roy, Senjuti Basu

A2 - Melnik, Sergey

A2 - Idreos, Stratos

A2 - Roy, Sudeepa

A2 - Rekatsinas, Theodoros

A2 - Leis, Viktor

A2 - Zhou, Wenchao

A2 - Gatterbauer, Wolfgang

A2 - Ives, Zack

PB - Association for Computing Machinery (ACM)

CY - New York

T2 - ACM SIGMOD/PODS International Conference on Management of Data, SIGMOD/PODS 2025

Y2 - 22 June 2025 through 27 June 2025

ER -

Chan TN, Zhu B, Wu D, Peng Y, U LH, Tu W et al. A Fast Line Density Visualization Plugin for Geographic Information Systems. In Deshpande A, Aboulnaga A, Salimi B, Chandramouli B, Howe B, Loo BT, Glavic B, Curino C, Zhe Wang D, Suciu D, Abadi D, Srivastava D, Wu E, Nawab F, Ilyas I, Naughton J, Rogers J, Patel J, Arulraj J, Yang J, Echihabi K, Ross K, Daudjee K, Lakshmanan L, Garofalakis M, Riedewald M, Mokbel M, Ouzzani M, Kennedy O, Kennedy O, Papotti P, Alvaro P, Bailis P, Miller R, Roy SB, Melnik S, Idreos S, Roy S, Rekatsinas T, Leis V, Zhou W, Gatterbauer W, Ives Z, editors, SIGMOD/PODS '25: Companion of the 2025 International Conference on Management of Data. New York: Association for Computing Machinery (ACM). 2025. p. 63–66. (Companion of the International Conference on Management of Data). doi: 10.1145/3722212.3725075

A Fast Line Density Visualization Plugin for Geographic Information Systems

Abstract

Publication series

Conference

User-Defined Keywords

UN SDGs

Access to Document

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