OASIS – un Outil d’Analyse de la Ségrégation et des Inégalités Spatiales

OasisR: An R Package to Bring Some Order to the World of Segregation Measurement

“…ArcInfo 7 4 Wong and Chong (1998) (Avenue) ArcView 3.2 7 Wong (1996Wong ( , 2003 The Oasis web platform developed by Tivadar et al (2014) is novel in not requiring any software installation, requests are made via the web navigator, and computation is conducted on distant servers. The user has the possibility of wide segregation analysis on a territory (including auto-correlation indices, descriptive statistics and web mapping) using either a historical French data base or their own data.…”

“…The first proximity indices were introduced by White (1983) for two groups, and later generalized to apply to more than two groups by White (1986): the mean proximity between the members of the same group P xx k (one-group index), and between two different groups P xy k 1 k 2 (between-group index), and the mean proximity between persons in the area without regard to the group P oo (multi-group index). In the original papers, White proposed considering the distance within a spatial unit as non-null, and advised a function of the area (0.6 √ A) but a null diagonal distance matrix is most commonly used in the literature and computed using software packages (Apparicio et al 2014;Tivadar et al 2014). In OasisR, the user can choose among these options or exploit a user value.…”

“…In the case of individual sampling units, each individual makes a random draw without replacement among spatial units, according to a probability vector. Location probability can be identical or constrained, e.g., by unit population (Cortese et al 1976), or by area (Tivadar et al 2014). As in Findlay and Findlay (1984) and Carrington and Troske (1997), we generate random localization patterns by resampling directly from the original data (aggregation of individual independent random draws) instead of sampling with theoretical distribution (Boisso et al 1994;Tivadar et al 2014).…”

“…Location probability can be identical or constrained, e.g., by unit population (Cortese et al 1976), or by area (Tivadar et al 2014). As in Findlay and Findlay (1984) and Carrington and Troske (1997), we generate random localization patterns by resampling directly from the original data (aggregation of individual independent random draws) instead of sampling with theoretical distribution (Boisso et al 1994;Tivadar et al 2014). If we set sampling on spatial units (Feitosa et al 2007;Tivadar et al 2014), we have a similar framework to the permutation test developed in spatial auto-correlation analysis (Anselin 1995).…”

“…In the new millennium, in addition to the development of spatial and local indices, developments have been made in the direction of multi-group indices (Reardon and Firebaugh 2002;Reardon and O'Sullivan 2004) and of specific measures for ordered groups (Reardon 2009), income segregation (Reardon 2011;Reardon and Bischoff 2011) and activity space (Wong and Shaw 2011;Farber, Páez, and Morency 2012;Farber, O'Kelly, Miller, and Neutens 2015). Also, efforts have been made to respond to the lack of statistical inference methods for segregation measures, such as bootstrap tests (Boisso, Hayes, Hirschberg, and Silber 1994;Lee, Minton, and Pryce 2015), randomization tests/Monte Carlo simulations (Feitosa, Câmara, Monteiro, Koschitzki, and Silva 2007;Tivadar, Schaeffer, Torre, and Bray 2014) and Bayesian inference (Lee et al 2015). For a review of these recent topics in segregation measurement, see Yao, Wong, Bailey, and Minton (2019).…”