PRiSm: Policy Recommendation Systems in Cadastral Survey Using National Public Opinion Big Data

Kihoon Lee - Korea Research Institute for Human Settlements, 5 Gukchaegyeonguwon-ro, Sejong-si, 30147, Korea
Yohan Chang - Korea Research Institute for Human Settlements, 5 Gukchaegyeonguwon-ro, Sejong-si, 30147, Korea


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.8.3-2.3023

Abstract


Cadastral surveys are vital for assessing individual properties and generating national statistics. In South Korea, rapid territorial changes have exposed personal-level issues, while current systems limited public engagement capabilities have constrained policymaking. Although efforts have been made to bridge this gap, they have been hindered by the lack of an effective medium. This study introduces a novel framework, PRiSm (Policy Recommendation Systems in Cadastral Survey), which leverages National Public Opinion Big Data. We collected and analyzed two key data sources: 1) public opinion data from 2018 to 2023, which correlates strongly with cadastral resurveys across South Korea, and 2) content from 54 major news media outlets over the same period. The first data set represents bottom-up opinions at the individual level, while the second reflects top-down perspectives on national issues. The PRiSm system, developed in this study, utilizes Natural Language Processing (NLP) and advanced Machine Learning (ML) techniques, including Word2Vec and a Genetic Algorithm for hyperparameter optimization, to process over a thousand inquiries and news articles. Our results highlight how different groups engage in discussions shaped by their interests and concerns, revealing key sensitivities and recommending terms invaluable for stakeholders and policymakers. We anticipate that PRiSm will offer meaningful insights for the public and decision-makers. Additionally, with more advanced ML and/or Deep Learning algorithms, there is significant potential for further advancements in NLP within the PRiSm framework.

Keywords


Cadastral survey; public opinion big data; natural language processing; policy recommendation systems; prediction

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References


H. J. Lee. “A Study on the Designate of Responsible Agency for Cadastral Resurvey,” Journal of the Korean Society of Cadastre, vol.39, no.3, pp. 131-114, 2023.

G. M. Shin, “A Study on Legal Relationship When Land for Sale is a Cadastral Disagreement Land,” Real Estate Law Review. vol.24, no.2, pp.1-29, 2020, doi: 10.32989/rel.2020.24.2.1.

J. H. Choi and J. W. Lee, “Research on the Improvement Method for Liquidation Amount Calculation System of Cadstral Resurvey: Comparison of Expert Perceptions,” Appraisal studies, vol.21, no.3, pp.33-57, 2022, doi: 10.23843/as.21.3.2.

Y. J. Lee, “A Study on the Application Plan of RPA for Cadastral Resurvey Projects,” Journal of the Korean Society of Cadastre, vol.37, no.3, pp.1-18, 2021, doi: 10.22988/ksc.2021.37.3.001.

P. Krigsholm, K. Riekkinen and P. Ståhle. “The Changing Uses of Cadastral Information: A User-Driven Case Study,” Land, vol.7, no.83, 2018, doi: 10.3390/land7030083.

C. Daugbjerg and A. Kay, “Policy feedback and pathways: when change leads to endurance and continuity to change,” Policy Sciences, vol.53, pp.253-268, 2020, doi:10.1007/s11077-019-09366-y.

K. H. Lee and D. H. Kim, “Analysis of Research Trends in the Cadastral Field Using Topic Modeling,” Journal of the Korean Society of Cadastre, vol.40, no.2, pp.71-82, 2024, doi:10.22988/ksc.2024.40.2.006.

G. Maria, P. Chryssy and I. Charalabos, “A technical solution for 3D crowdsourced cadastral surveys,” Land Use Policy, vol.98, 2020, doi:10.1016/j.landusepol.2019.104419.

D. Paolo, “The usability of GNSS mass-market receivers for cadastral surveys considering RTK and NRTK techniques,” Geodesy and Geodynamics, vol.10, no.4, pp. 282-289, 2019, doi:10.1016/j.geog.2019.04.006.

Z. Sandra and N. Gerhard, “Cadastral Surveys Using Terrestrial Laser Scanning – Accuracy and Economy,” Geodetski Vestnik, vol.67, no.4, pp. 473-486, 2023, doi: 10.15292/geodetski-vestnik.2023.04.473-486.

K. H. Lee, C. Y. Baek and S. H. Kwon, “A Study on Cadastral Surveying of Forest Areas Using GNSS Methods, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography, vol.42, no.4, 2024, pp.256-264, doi:10.7848/ksgpc.2024.42.4.253.

G. Dogus, “3D modelling of subsurface legal spaces and boundries for 3D ladn administration,” Tunneling and Underground Space Technoogy, vol. 152, 105956, 2024, doi: 10.1016/j.tust.2024.105956.

S. M. Kim and J. Heo, “Development of 3D underground cadastral data model in Korea: Based on land administration domain model,” Land Use Policy, vol.60, pp. 123-138, 2017, doi:10.1016/j.landusepol.2016.10.020.

M. P. Jesper and P. Jenny, “Trends in 3D cadaster – A literature survey,” Land use Policy, vol.131, 106716, 2023. doi:10.1016/j.landusepol.2023.106716.

N. Vučić, M. Mađer, S. Vranić and M. Roić, “Initial 3D cadaster registration by cadastral resurvey in the Republic of Croatia,” Land Use Policy, vol.98, 104335, 2020, doi:10.1016/j.landusepol.2019.104335.

S. M. Kim and J. Heo, “Registration of 3D underground parcel in Korean cadastral system,” Cities, vol. 89, pp.105-119, 2019, doi:10.1016/j.cities.2019.01.027.

R. Hajji, H. E. Asri and C. E. Zriouli, “Upgrading to 3D cadaster in Morocco: Lessons learned from benchmarking of international 3D cadastral systems,” Land Use Policy, vol 128, 106605, 2023, doi:10.1016/j.landusepol.2023.106605.

G. Maria and P. Chryssy, “3D crowdsourced parametric cadastral mapping: Pathways integrating BIM/IFC crowdsourced adata and LADM,” Land Use Policy, vol. 131, 106713, 2023, doi:10.1016/j.landusepol.2023.106713.

A. Behnam, R. Abbas and O. Hamed, “Proposing a multi-jurisdictional framework for 3D digital cadaster in Australia and New Zealand,” Land Use Policy, vol.131, 106714, 2023, doi:10.1016/j.landusepol.2023.106714.

F. C. Yu, C. P. Chung, H. Y. Lee and C. P. Chen. “UAS Aerial Surveying in Resurvey Process of Cadastral Maps,” 2023 IEEE 5th Eurasia Conference on IOT, Communication and Engineering (ECICE), Yunlin, Taiwan, 2023, doi:10.1109/ECICE59523.2023.10383170.

C. potsiou, N. Doulamis, N. Bakalos, M. Gkeli, C. Ioannidis, and S. Markouizou. “A Prototype Machine Learning Tool Aiming to Support 3D Crowdsourced Cadastral Surveying of Self-Made Cities.” Land ,vol.12, no.1, 2023, doi: 10.3390/land12010008.

B. Tareke, M. Koeva and C. Persello. “Extracting Polygons of Visible Cadastral Boundaries Using Deep Learning,” IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 2023, doi:10.1109/IGARSS52108.2023.10282644.

V. Ostankovich and I. Afanasyev. “Illegal Buildings Detection from Satellite Images using GoogLeNet and Cadastral Map,” 2018 International Conference on Intelligent Systems (IS), Funchal, Portugal, 2018, doi: 10.1109/IS.2018.8710565.

R. Petitpierre and P. Guhennec. “Effective annotation for the automatic vectorization of cadastral maps,” Digital Scholarship in the Humanities, vol.38, no.3, 2023, doi: 10.1093/llc/fqad006.

A. R. Song, S. A. Park and Y. I. Kim, “Updating cadastral maps using deep convolutional networks and hyperspectral imaging,” Paper presented at 40th Asian Conference on Remote Sensing: Progress of Remote Sensing Technology for Smart Future, ACRS 2019, 2019.

Y. J. Joo and D. H. Kim. “The Big Data Analytics Regarding the Cadastral Resurvey News Articles,” Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, vol.32, no.6, pp.651-659, 2014, doi: 10.7848/ksgpc.2014.32.6.651.

T. I. Cho, B. G. Choi, Y. W. Na, Y. S. Moon, and S. H. Kim, “A Suggestion for Spatiotemporal Analysis Model of Complaints on Officially Assessed Land Price by Big Data Mining,” Journal of Cadastre & Land InformatiX, vol.48, no.2, pp.79-98, 2018, doi:10.22640/lxsiri.2018.48.2.79.

L. Ma, Y. Zhang. “Using Word2Vec to process big text data.” In2015 IEEE International Conference on Big Data (Big Data), IEEE Oct. 29, pp. 2895-2897, 2015, doi: 10.1109/BigData.2015.7364114.

S. Sivakumar, L.S. Videla, T.R. Kumar, J. Nagaraj, S. Itnal, and D. Haritha. "Review on word2vec word embedding neural net." 2020 international conference on smart electronics and communication (ICOSEC). IEEE, 2020, doi: 10.1109/ICOSEC49089.2020.9215319.

Z. Wang, M. Wang, H. Shen, and Y. Han. “Application of Sentiment Classification of Weibo Comments Based on TextCNN Model.” In 2022 International Conference on Computer Network, Electronic and Automation (ICCNEA), IEEE, pp. 223-228, 2022, doi:10.1109/ICCNEA57056.2022.00057.

Y. Luo, F. Wang, F. Zhao, J. Guo, L. Wang, Y. Hao, and D.D. Zeng. "A Framework for Policy Information Popularity Prediction in New Media," 2019 IEEE International Conference on Intelligence and Security Informatics (ISI), Shenzhen, China, pp. 209-211, 2019, doi:10.1109/ISI.2019.8823415.

A. Shuklin, D. Parygin, A. Gurtyakov, O. Savina and N. Rashevskiy, "Synthetic News as a Tool for Evaluating Urban Area Development Policies," 2022 International Conference on Engineering and Emerging Technologies (ICEET), Kuala Lumpur, Malaysia, pp. 1-6, 2022, doi: 10.1109/ICEET56468.2022.10007405.

"Public Data Portal," data.go.kr, [Online]. Available: https://www.data.go.kr/en/index.do. [Accessed: Jul. 9, 2024].

"Bigkinds," bigkinds.or.kr, [Online]. Available: https://www.bigkinds.or.kr. [Accessed: Jul. 9, 2024].

P. Kalgotra, R. Sharda, and A. Luse. “Which similarity measure to use in network analysis: Impact of sample size on phi correlation coefficient and Ochiai index.” International Journal of Information Management, vol.55, p.102229, 2020, doi:10.1016/j.ijinfomgt.2020.102229.

T. Mikolov, K. Chen, G. Corrado, and J. Dean. “Efficient estimation of word representations in vector space.” arXiv preprint arXiv:1301.3781, 2013, 10.48550/arXiv.1301.3781.

X. Rong, “word2vec parameter learning explained.” arXiv preprint arXiv:1411.2738, 2014, 10.48550/arXiv.1411.2738.

Y. Goldberg, and O. Levy, “word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method.” arXiv preprint arXiv:1402.3722, 2014, 10.48550/arXiv.1402.3722.

A. Khan, K. Khan, W. Khan, S. N. Khan, and R. Haq, “Knowledge-based Word Tokenization System for Urdu”, Journal of Informatics and Web Engineering, vol. 3, no. 2, pp. 86–97, 2024. doi:10.33093/jiwe.2024.3.2.6.

F. Zaman et al., “Intelligent Abstractive Summarization of Scholarly Publications with Transfer Learning”, Journal of Informatics and Web Engineering, vol. 3, no. 3, pp. 256–270, 2024. doi:10.33093/jiwe.2024.3.3.16.