Effects of missing data and locational errors on spatial concentration measures based on Ripley's K-function. Issue 2 (3rd July 2017)

• Record Type: Journal Article
• Authors: Arbia, Giuseppe; Espa, Giuseppe; Giuliani, Diego; Dickson, Maria Michela
• Is Part Of: Spatial economic analysis. Volume 12:Issue 2/3(2017)
• Page Start: 326
• Page End: 346
• Publication Date: 2017-07-03
• Subjects: spatial microeconometrics -- spatial concentration -- Ripley's K-function -- geomasking -- confidentiality -- missing spatial data; 空间微观计量经济 -- 空间集中度 -- 雷普利K函数 -- 地理模糊处理 -- 匿名性 -- 遗失的空间数据; micro-économétrie spatiale -- concentration spatiale -- fonction K de Ripley -- géo-masquage -- confidentialité -- données spatiales manquantes; microeconometría espacial -- concentración espacial -- función K de Ripley -- geo-enmascaramiento -- confidencialidad -- datos espaciales incompletos; C2 -- C21 -- O1 -- O12 -- R1 -- R12; Space in economics -- Periodicals; Economic geography -- Periodicals; Space in economics -- Econometric models -- Periodicals; Urbanization -- Periodicals; Espace (Économie politique) -- Périodiques; Économétrie -- Périodiques; Géographie économique -- Périodiques; Économie industrielle -- Périodiques; Économie du travail -- Périodiques; Économie urbaine -- Périodiques; Politique économique -- Périodiques; Relations économiques internationales -- Périodiques; 330.9
• DOI: 10.1080/17421772.2017.1297479
• Languages: English
• ISSNs: 1742-1772

Abstract

ABSTRACT: Effects of missing data and locational errors on spatial concentration measures based on Ripley's K -function. Spatial Economic Analysis . Measures based on Ripley's K -function are the preferred tools to test the concentration of individual agents in an economic space. In many empirical cases, however, the datasets contain different inaccuracies due to missing data or uncertainty about the location of the agents. Little is known thus far about the effects of these inaccuracies on the K -function. This paper sheds light on the problem through a theoretical analysis supported by Monte Carlo experiments. The results show that patterns of clustering or inhibition may be observed not as genuine phenomena but only as the effect of data imperfections.