The Association Between SOC and Land Prices Considering Spatial Heterogeneity Based on Finite Mixture Modeling
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
An understanding of how Social Overhead Capital (SOC) is associated with the land value of the local community is important for effective urban planning. However, even within a district, there are multiple sections used for different purposes; the term for this is spatial heterogeneity. The spatial heterogeneity issue has to be considered when attempting to comprehend land prices. If there is spatial heterogeneity within a district, land prices can be managed by adopting the spatial clustering method. In this study, spatial attributes including SOC, socio-demographic features, and spatial information in a specific district are analyzed with Finite Mixture Modeling (FMM) in order to find (a) the optimal number of clusters and (b) the association among SOCs, socio-demographic features, and land prices. FMM is a tool used to find clusters and the attributes’ coefficients simultaneously. Using the FMM method, the results show that four clusters exist in one district and the four clusters have different associations among SOCs, demographic features, and land prices. Policymakers and managerial administration need to look for information to make policy about land prices. The current study finds the consideration of closeness to SOC to be a significant factor on land prices and suggests the potential policy direction related to SOC.
Abstract
An understanding of how Social Overhead Capital (SOC) is associated with the land value of the local community is important for effective urban planning. However, even within a district, there are multiple sections used for different purposes; the term for this is spatial heterogeneity. The spatial heterogeneity issue has to be considered when attempting to comprehend land prices. If there is spatial heterogeneity within a district, land prices can be managed by adopting the spatial clustering method. In this study, spatial attributes including SOC, socio-demographic features, and spatial information in a specific district are analyzed with Finite Mixture Modeling (FMM) in order to find (a) the optimal number of clusters and (b) the association among SOCs, socio-demographic features, and land prices. FMM is a tool used to find clusters and the attributes’ coefficients simultaneously. Using the FMM method, the results show that four clusters exist in one district and the four clusters have different associations among SOCs, demographic features, and land prices. Policymakers and managerial administration need to look for information to make policy about land prices. The current study finds the consideration of closeness to SOC to be a significant factor on land prices and suggests the potential policy direction related to SOC.
참고문헌 (41)
- Biernacki, C., "An improvement of the NEC criterion for assessing the number of clusters in a mixture model", Pattern Recognition Letters, 20(3), pp.267~272 (1999)
- Brown, M. E., "Identifying individual social capital profiles in low-resource communities: Using cluster analysis to enhance community engagement", Journal of the Society for Social Work and Research, 10(4), pp.477~500 (2019)
- Buddhavarapu, P., "Modeling unobserved heterogeneity using finite mixture random parameters for spatially correlated discrete count data", Transportation Research Part B: Methodological, 91, pp.492~510 (2016)
- "Soaring home prices stroke anger against Korea’s president" (2021)[URL]
- 김솔희, "헤도닉모형을 이용한 농촌지역 생활편익시설의 접근성이 지가에 미치는 영향 분석", 농촌계획, 22(1), pp.93~100 (2016), 한국농촌계획학회[DOI]
- Choi, Y., "Comparative analysis of spetial impact of living social overhead capital on housing price by residential type", Transporting Engineering, 25, pp.1056~1065 (2021)[DOI]
- Cootner, P. H., "The economics of take-offinto sustained growth. International Economic Association Series", pp.261~284 (1963), Palgrave Macmillan
- Cottineau, C., "Defining urban clusters to detect agglomeration economies", Environment and Planning B: Urban Analytics and City Science, 46(9), pp.1611~1626 (2019)
- Dong, L., "Population-weighted efficiency in transportation networks", Scientific Report, 6 (2016)
- Faghih-Imani, A., "A finite mixture modeling approach to examine New York City bicycle sharing system (CitiBike) users’ destination preferences", Transportation, 47, pp.529~553 (2017)
- Fotheringham, A. S., "Directional variation in distance decay", Environment and Planning A: Economy and Space, 27(5), pp.715~729 (1995)
- Granovetter, M., "Economic action and social structure: The problem of embeddedness", American Journal of Sociology, 91(3), pp.481~510 (1985)
- Glumac, B., "A hedonic urban land price index", Land Use Policy, 81, pp.802~812 (2019)
- Hair, J. F., "Advanced Issues in Partial Least Squares Structural Equation Modeling" (2018), Sage
- Jiao, L., "Analyzing the spatial autocorrelation of regional urban datum land price", Geo-spatial Information Science, 15(4), pp.263~269 (2012)
- Kurihara, K. K., "Social overhead capital and balanced economic growth", Social and Economic Studies, 19(3), pp.398~405 (1970)
- Lanza, S. T., "Latent class analysis: An alternative perspective on subgroup analysis in prevention and treatment", Prevention Science, 14(2), pp.157~168 (2013)
- Leisch, F., "FlexMix: A general framework for finite mixture models and latent class regression in R", Journal of Statistical Software, 11(8), pp.1~18 (2004)
- Liu, Q., "A density-based spatial clustering algorithm considering both spatial proximity and attribute similarity", Computers & Geosciences, 46, pp.296~309 (2012)
- McLachlan, G. J., "Comprehensive Chemometrics", pp.655~681 (2009), Elsevier
- Maddison, J. D., "A hedonic analysis of agricultural land prices in England and Wales", European Review of Agricultural Economics, 27(4), pp.519~532 (2000)
- "Ministry of Land, Infrastructure a Guideline to Analyze the Report. A Report of Supplying Basic Infrastructure and Transport" (2019)[URL]
- Naughton, L., "Geographical narratives of social capital: Telling different stories about the socio-economy with context, space, place, power and agency", Progress in Human Geography, 38(1), pp.3~21 (2013)
- Nest, G., "An overview of mixture modelling for latent evolutions in longitudinal data: Modeling approaches, fit statistics and software", Advances in Life Course Research, 43, pp.100323~ (2020)[DOI]
- Nocco, A., "Preference heterogeneity and economic geography", Journal of Regional Science, 49(1), pp.33~56 (2009)
- Oller, R., "Monocentricity and directional heterogeneity: A conditional parametric approach", Geographical Analysis, 49(3), pp.343~361 (2016)
- Ottaviano, G. I. P., "‘New’ new economic geography: Firm heterogeneity and agglomeration economies", Journal of Economic Geography, 11(2), pp.231~240 (2011)
- "Social Overhead Capital"[URL]
- Parr, J. B., "Spatial definitions of the city: Four perspectives", Urban Studies, 44(2), pp.381~392 (2007)
- Peel, D., "Robust mixture modelling using the t distribution", Statistics and Computing, 10(4), pp.339~348 (2000)
- Rosen, S., "Hedonic prices and implicit markets: Product differentiation in pure competition", Journal of Political Economy, 82(1), pp.34~55 (1974)
- Sampson, R. J., "Neighborhood social capital as differential social organization: Resident and leadership dimensions", American Behavioral Scientist, 51(11), pp.1579~1605 (2009)
- Sarstedt, M., "A review of recent approaches for capturing heterogeneity in partial least squares path modelling", Journal of Modeling in Management, 3(2), pp.140~161 (2008)
- Sheppard, E., "Geography, nature, and the question of development", Dialogues in Human Geography, 1(1), pp.46~75 (2011)
- Simon, S., "A modern approach to regression with R. Springer Texts in Statistics" (2009), Springer
- StataCorp, "Stata Finite Mixture Models Reference Manual. Release 17", A Stata Press Publication, ~ (2021)
- 서교, "헤도닉분석기법과 공간계량경제모형을 이용한 농촌지역 지가의 영향인자 분석", 농촌계획, 11(3) (2005), 한국농촌계획학회
- Tabuchi, T., "Taste heterogeneity, labor mobility and economic geography", Journal of Development Economics, 69(1), pp.155~177 (2002)
- Vermunt, J. K., "Latent class cluster analysis", Applied Latent Class Analysis, 11, pp.89~106 (2002)
- Wedel, M., "Market Segmentation: Conceptual and Methodological Foundations" (2000), Kluwer Academic Publishers