diff --git a/DESCRIPTION b/DESCRIPTION index e4bed35..0c27f83 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: MTA Title: Multiscalar Territorial Analysis -Version: 1.0 +Version: 0.1.0 Date: 2017-02-28 Authors@R: c(person("Ronan", "Ysebaert", email = "ronan.ysebaert@cnrs.fr", role = c("aut")), @@ -10,7 +10,7 @@ Authors@R: c(person("Ronan", "Ysebaert", email = "timothee.giraud@cnrs.fr", role = c("aut", "cre"))) Description: Build multiscalar territorial analysis based on various contexts. License: GPL-3 -URL: https://github.com/Groupe-ElementR/MTA/ +URL: https://github.com/riatelab/MTA/ BugReports: https://github.com/Groupe-ElementR/MTA/issues/ LazyData: true Depends: diff --git a/R/package.R b/R/package.R index acec0b7..36e5366 100644 --- a/R/package.R +++ b/R/package.R @@ -15,8 +15,8 @@ #' redistributions based on the three deviations. #' } #' -#' @references YSEBAERT R. (et al.), 2011, HyperAtlas, un outil scientifique au -#' service du débat politique - Application à la politique de cohésion de l’Union Européenne, congrès CIST, Collège International des Sciences du Territoire (Paris). +#' @references GRASLAND C., YSEBAERT R., ZANIN C., LAMBERT N., Spatial disparities in Europe (Chapter 4) +#' in GLOERSEN E., DUBOIS A. (coord.), 2007, Regional disparities and cohesion: What Strategies for the future?, DG-IPOL – European Parliament. #' #' @docType package NULL diff --git a/R/tdev.R b/R/tdev.R index e5cbbe2..f901b17 100644 --- a/R/tdev.R +++ b/R/tdev.R @@ -20,7 +20,7 @@ #' moved to obtain the ratio of the aggregated level on all belonging units. #' @return A vector is returned. #' @examples -#' # load dat +#' # load data #' data("GrandParisMetropole") #' # compute absolute territorial deviation (EPT level) #' com$tdevabs <- tdev(x = com, var1 = "INC", var2 = "TH", type = "abs", @@ -40,12 +40,12 @@ #' pal2 = "wine.pal", n2 = 3) #' # plot a choropleth map of the relative territorial deviation #' choroLayer(spdf = com.spdf, df = com, var = "tdevrel", -#' legend.pos = "topright", +#' legend.pos = "topleft", #' breaks = bks, border = NA, #' col = cols) #' # add symbols proportional to the absolute territorial deviation #' propSymbolsLayer(spdf = com.spdf, df = com, var = "tdevabs", -#' legend.pos = "right",legend.values.rnd = -5, +#' legend.pos = "left",legend.values.rnd = -5, #' col = "#ff000050",col2 = "#0000ff50", #' legend.style = "c", inches = 0.2, #' breakval = 0) diff --git a/man/MTA.Rd b/man/MTA.Rd index 13e0c20..520d966 100644 --- a/man/MTA.Rd +++ b/man/MTA.Rd @@ -21,6 +21,6 @@ redistributions based on the three deviations. } } \references{ -YSEBAERT R. (et al.), 2011, HyperAtlas, un outil scientifique au -service du débat politique - Application à la politique de cohésion de l’Union Européenne, congrès CIST, Collège International des Sciences du Territoire (Paris). +GRASLAND C., YSEBAERT R., ZANIN C., LAMBERT N., Spatial disparities in Europe (Chapter 4) +in GLOERSEN E., DUBOIS A. (coord.), 2007, Regional disparities and cohesion: What Strategies for the future?, DG-IPOL – European Parliament. } diff --git a/man/tdev.Rd b/man/tdev.Rd index 433cf1e..2b92d17 100644 --- a/man/tdev.Rd +++ b/man/tdev.Rd @@ -37,7 +37,7 @@ The absolute territorial deviation is the amount of numerator that could be moved to obtain the ratio of the aggregated level on all belonging units. } \examples{ -# load dat +# load data data("GrandParisMetropole") # compute absolute territorial deviation (EPT level) com$tdevabs <- tdev(x = com, var1 = "INC", var2 = "TH", type = "abs", @@ -57,12 +57,12 @@ if(require('cartography')){ pal2 = "wine.pal", n2 = 3) # plot a choropleth map of the relative territorial deviation choroLayer(spdf = com.spdf, df = com, var = "tdevrel", - legend.pos = "topright", + legend.pos = "topleft", breaks = bks, border = NA, col = cols) # add symbols proportional to the absolute territorial deviation propSymbolsLayer(spdf = com.spdf, df = com, var = "tdevabs", - legend.pos = "right",legend.values.rnd = -5, + legend.pos = "left",legend.values.rnd = -5, col = "#ff000050",col2 = "#0000ff50", legend.style = "c", inches = 0.2, breakval = 0) diff --git a/vignettes/MTA.Rmd b/vignettes/MTA.Rmd index d058cdb..60ad985 100644 --- a/vignettes/MTA.Rmd +++ b/vignettes/MTA.Rmd @@ -6,7 +6,7 @@ output: rmarkdown::html_vignette: toc: yes vignette: > - %\VignetteIndexEntry{MTA Historical and Conceptual Background} + %\VignetteIndexEntry{Historical and Conceptual Background} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- @@ -17,7 +17,7 @@ vignette: > It is impossible to understand the origin of the implementation of the MTA package without taking into account the specificities of spatial planning in Europe and the **need of monitoring tools and methods for measuring inequalities in Europe**: Early the question of measurement of territorial inequalities has been raised by planners and policy makers. Without being exhaustive, let us remind from a political point of view some usages of statistics and cartography regarding the measure of territorial inequalities in a European context. -```{r fig.width=10,echo=FALSE} +```{r fig.width=7,echo=FALSE} library(png) library(grid) img <- readPNG("./img/figure_MTA1.png") @@ -34,7 +34,7 @@ The analysis of these two maps reveals several possibilities for measuring terri Nowadays, the need to measure territorial disparities at lower scales become more and more important. The historical planning issues managed at national level is in a large extent transferred to regions and local authorities. To act locally, these new territories of governance requires statistical evidences to understand the structure and the dynamics of their territories. As displayed below, a lot of urban agencies and experts financed by public fundings were recently created to provide local pictures of local dynamics (Metropole du Grand Paris, Metropole du Grand Lyon, Greater London, etc.). Here again, the need of territorial information is high. -```{r fig.width=10,echo=FALSE} +```{r fig.width=7,echo=FALSE} img <- readPNG("./img/figure_MTA2.png") grid.raster(img) ``` @@ -53,7 +53,7 @@ MTA methods have been developed in order to highlight in a simple way such situa The central hypothesis behind the MTA consists to consider that the meaning of a statistical indicator is always dependant of territorial context of reference. Taking a concrete example, knowing that the Gross Domestic Product in 2008 of Nord-Pas-de-Calais is 24 700 euros per capita provides few information itself. It is rather interesting to understand how this region stands as regard to the European Union average (22 800 euros, + 7,7 %), to its country of belonging (France, 30 400 euros, - 18 %) or as compared to its neighbouring regions (24 950 euros, - 1 %). The combination of these deviation measures allows to highlight regions in favourable situations, lagging regions and also to depict contractory situations (e.g. a rich region in a poor country, and vice-versa). -```{r fig.width=10,echo=FALSE} +```{r fig.width=7,echo=FALSE} img <- readPNG("./img/figure_MTA3.png") grid.raster(img) ``` diff --git a/vignettes/MTA_Scenario.Rmd b/vignettes/MTA_Scenario.Rmd index c26723d..bc8f460 100644 --- a/vignettes/MTA_Scenario.Rmd +++ b/vignettes/MTA_Scenario.Rmd @@ -6,7 +6,7 @@ output: rmarkdown::html_vignette: toc: yes vignette: > - %\VignetteIndexEntry{MTA Scenario - Income Inequalities in the Metropolis of Greater Paris} + %\VignetteIndexEntry{Scenario - Income Inequalities in the Metropolis of Greater Paris} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- @@ -224,16 +224,6 @@ in the Western part of Paris and its suburbs. - ------------------------------------------------- - -*J'ai lu jusque là.* Après je n'ai que bossé sur le code. - ------------------------------------------------- - - - - # 4. Intoducing the MTA Functions ## 4.1 General, territorial and spatial deviations @@ -293,7 +283,6 @@ This part proposes some graphical outputs helping to have an idea regarding ineq The code below takes in entry the numerator (INC) and the denominator (TH) and returns the global deviation indicators (relative and absolute). These indicators are afterwards associated with the input SpatialDataFrame (com.spdf) for displaying on map this deviation. ````{r gdevrel_plot, fig.width=7, fig.height=5, warning = FALSE, cache = TRUE} -dev.off() # general relative deviation com$gdevrel <- gdev(x = com, var1 = "INC", @@ -313,8 +302,8 @@ com$gdevabsmil <- com$gdevabs / 1000000 par(mar = c(0,0,1.2,0)) # Plot territories -plot(com.spdf, col = "grey70", border="#EDEDED",lwd=0.25) -plot(ept.spdf,border="#1A1A19",lwd=1,add=T) +plot(com.spdf, col = "grey70", border = "#EDEDED", lwd = 0.25) +plot(ept.spdf, border = "#1A1A19", lwd = 1, add = TRUE) # Global deviation (relative and absolute) cartography propSymbolsChoroLayer(spdf = com.spdf, df = com,legend.var.values.rnd = 4, @@ -333,10 +322,6 @@ propSymbolsChoroLayer(spdf = com.spdf, df = com,legend.var.values.rnd = 4, legend.var2.title.txt = "Deviation to the global context (100 = Metropole du Grand Paris average)", legend.var.style = "e") - - - - layoutLayer(title = "Global deviation - Tax income per households", sources = "GEOFLA® 2015 v2.1, Apur, impots.gouv.fr", author = "RIATE, 2016", @@ -422,17 +407,17 @@ The code below takes in entry the numerator (INC) and the denominator (TH) and r # Territorial relative deviation calculation com$mdevrel <- tdev(x = com, - var1 = "INC", - var2 = "TH", - type = "rel", - key = "LIBEPT") + var1 = "INC", + var2 = "TH", + type = "rel", + key = "LIBEPT") # Territorial absolute deviation calculation com$mdevabs <- tdev(x = com, - var1 = "INC", - var2 = "TH", - type = "abs", - key = "LIBEPT") + var1 = "INC", + var2 = "TH", + type = "abs", + key = "LIBEPT") # Territorial deviation in million Euros com$mdevabsmil <- com$mdevabs / 1000000 @@ -453,7 +438,7 @@ layoutLayer(title = "Territorial deviation - Tax income per households, 2013", # Plot territories plot(com.spdf, col = "grey70", border="#EDEDED",lwd=0.25,add=T) -plot(ept.spdf,border="#1A1A19",lwd=1,add=T) +plot(ept.spdf, border = "#1A1A19", lwd = 1, add = T) # Territorial deviation (relative and absolute) cartography propSymbolsChoroLayer(spdf = com.spdf, df = com, @@ -567,24 +552,24 @@ par(mar=c(2,4,0,0)) # Spatial relative deviation calculation com$ldevrel <- sdev(spdf = com.spdf, - x = com, - spdfid = "DEPCOM", - xid = "DEPCOM", - var1 = "INC", - var2 = "TH", - order = 1, - type = "rel") + x = com, + spdfid = "DEPCOM", + xid = "DEPCOM", + var1 = "INC", + var2 = "TH", + order = 1, + type = "rel") # Spatial absolute deviation calculation com$ldevabs <- sdev(spdf = com.spdf, - x = com, - spdfid = "DEPCOM", - xid = "DEPCOM", - var1 = "INC", - var2 = "TH", - order = 1, - type = "abs") + x = com, + spdfid = "DEPCOM", + xid = "DEPCOM", + var1 = "INC", + var2 = "TH", + order = 1, + type = "abs") # Spatial deviation in million Euros com$ldevabsmil <- com$ldevabs / 1000000 @@ -746,10 +731,10 @@ abline(h=0, col = "red") ab.standard <- com[res.standard < -seuil.standard | res.standard > +seuil.standard,] for (i in 1:nrow(ab.standard)){ -# Take the territorial units listing below and above the threshold -communes <- row.names(ab.standard)[i] -# Plot the residual names -text(com[communes,"ldevrel"],res.standard[communes],communes,cex =0.5, pos=4) + # Take the territorial units listing below and above the threshold + communes <- row.names(ab.standard)[i] + # Plot the residual names + text(com[communes,"ldevrel"],res.standard[communes],communes,cex =0.5, pos=4) } abline (v = seq(50,200,10), col = "gray70", lwd = 0.25, lty = 3) @@ -789,17 +774,17 @@ dfSpa <- data.frame(dist = seq(0,35000,by=500), ineq = NA) # Calculation of spatial deviations in absolute terms for all the distances retained spat <- function(x){ - ldevabs <- sdev(spdf = com.spdf, - x = com, - spdfid = "DEPCOM", - xid = "DEPCOM", - var1 = "INC", - var2 = "TH", - dist = x[1], - type = "abs") + ldevabs <- sdev(spdf = com.spdf, + x = com, + spdfid = "DEPCOM", + xid = "DEPCOM", + var1 = "INC", + var2 = "TH", + dist = x[1], + type = "abs") ldevabspos <- ldevabs[ldevabs>0] -sum(ldevabspos) + sum(ldevabspos) } dfSpa$ineq <- apply(dfSpa, 1, spat) @@ -814,17 +799,17 @@ dfOrder <- data.frame(order = seq(0,20,by=1), ineq = NA) # Calculation of spatial deviations in absolute terms for all the contiguities retained spat <- function(x){ - ldevabs <- sdev(spdf = com.spdf, - x = com, - spdfid = "DEPCOM", - xid = "DEPCOM", - var1 = "INC", - var2 = "TH", - order = x[1], - type = "abs") + ldevabs <- sdev(spdf = com.spdf, + x = com, + spdfid = "DEPCOM", + xid = "DEPCOM", + var1 = "INC", + var2 = "TH", + order = x[1], + type = "abs") ldevabspos <- ldevabs[ldevabs>0] -sum(ldevabspos) + sum(ldevabspos) } dfOrder$ineq <- apply(dfOrder, 1, spat) @@ -842,14 +827,14 @@ abline (v = seq(0,22000,1000), col = "gray30", lwd = 0.25) abline (h = seq(0,35,5), col = "#488b37", lwd = 0.25) lines(dfSpa$dist ~ dfSpa$ineq,type='b',col='#488b37',lwd=0.25, lty = 1, pch = 20) title(xlab = "Mass of numerator to redistribute required to reach the equilibrium (million Euros)") -title(ylab="Neighbourhood: Euclidian distance (km)") +mtext("Neighbourhood: Euclidian distance (km)", col = "#488b37", side = 2,line = 2) # Adding plot 2 : contiguity plot.window(xlim=c(0,22500),ylim=c(0,20),xaxs="i", yaxs="i") axis(4, col ='#7a378b') abline (h = seq(0,20,5), col = "#7a378b", lwd = 0.25, lty = 1) lines(dfOrder$order~dfOrder$ineq,type='b',col='#7a378b',lwd=0.25, lty = 1, pch = 20) -mtext("Neighbourhood: Contiguity order", side = 4) +mtext("Neighbourhood: Contiguity order", side = 4, col = "#7a378b") #title for the graph title(main="Numerator redistribution as regards to spatial and contiguity parameters") @@ -898,8 +883,8 @@ par(mar=c(2,4,0,0)) # Compute the synthesis DataFrame (relative deviations) mst <- mst(spdf = com.spdf, x = com, spdfid = "DEPCOM", xid = "DEPCOM", - var1 = "INC", var2 = "TH", dist = NULL, key = "EPT", order = 1, - mat = NULL, threshold = 125, superior = TRUE) + var1 = "INC", var2 = "TH", dist = NULL, key = "EPT", order = 1, + mat = NULL, threshold = 125, superior = TRUE) # Plot layout par(mfrow = c(1,1), mar = c(0,0,1.2,0)) @@ -914,22 +899,23 @@ layoutLayer(title = "Synthesis / 3 contexts - Tax income per households, 2013", south = FALSE, extent = com.spdf) +# Colours Typology and legend layout +colours <- c("#f0f0f0", "#fdc785","#ffffab","#fba9b0", + "#addea6","#ffa100","#fff226","#e30020") + + # Plot typology map typoLayer(spdf = com.spdf, df = mst, var = "mst", add = TRUE, border = "#D9D9D9", - lwd=0.25, - col = levels(mst$colours), + lwd=0.25,legend.values.order = 0:7, + col = colours, legend.pos = "n") # Plot territorial level plot(ept.spdf,border="#252525",lwd=0.5,add=T) # Plot the legend -# Colours Typology and legend layout -colours <- c("#f0f0f0", "#fdc785","#ffffab","#fba9b0", - "#addea6","#ffa100","#fff226","#e30020") - rVal<-c(" . . . ","[X] . . ", " . [X] . ","[X] [X] . ", " . . [X]","[X] . [X]", @@ -947,29 +933,27 @@ More precisely (extract of the tables below), 11 communes are characterised by a 8 communes can be considered as "locally" advantaged (index above 125% only in a spatial context and/or in a spatial and a territorial context, coloured in green and yellow). These local poles of wealth are mainly located in the periphery of the MGP area (Le Raincy, Rungis, Coubron, etc), closed to poorer communes. -````{r synthesis125_class7, fig.width=7, fig.height=5, warning = FALSE, cache = TRUE, eval = FALSE} +````{r synthesis125_class7, fig.width=7, fig.height=5, warning = FALSE, cache = TRUE, eval = TRUE} # Communes in favorable situation for the three contexts -subset(mst, mst == 7, select = c(gdevrel, mdevrel, ldevrel, mst)) - +subset(mst, mst == 7, select = c(gdevrel, tdevrel, sdevrel, mst)) # Communes in favorable situation in a global context or in a global and in an territorial contexts -subset(mst, mst == 1 | mst == 3, select = c(gdevrel, mdevrel, ldevrel, mst)) - +subset(mst, mst == 1 | mst == 3, select = c(gdevrel, tdevrel, sdevrel, mst)) # Communes in favorable situation in a spatial context or in a spatial and a territorial context -subset(mst, mst == 4 | mst == 6, select = c(gdevrel, mdevrel, ldevrel, mst)) +subset(mst, mst == 4 | mst == 6, select = c(gdevrel, tdevrel, sdevrel, mst)) ```` ### 8.1.2 Communes in lagging situation The second step consists in analysing the reverse situation: the communes in lagging situation for this indicator. Thus, the mst function takes '80' for the threshold argument and 'FALSE' for the superior argument. This typology will specify the communes situated below 20% of the average for the global and/or the territorial and/or the spatial contexts. -````{r synthesislow, fig.width=7, fig.height=5, warning = FALSE, cache = TRUE, eval = FALSE} +````{r synthesislow, fig.width=7, fig.height=5, warning = FALSE, cache = TRUE, eval = TRUE} par(mar=c(2,4,0,0)) # Compute the synthesis DataFrame (relative deviations) mst <- mst(spdf = com.spdf, x = com, spdfid = "DEPCOM", xid = "DEPCOM", - var1 = "INC", var2 = "TH", dist = NULL, key = "EPT", order = 1, - mat = NULL, threshold = 80, superior = FALSE) + var1 = "INC", var2 = "TH", dist = NULL, key = "EPT", order = 1, + mat = NULL, threshold = 80, superior = FALSE) # Plot layout par(mfrow = c(1,1), mar = c(0,0,1.2,0)) @@ -984,22 +968,23 @@ layoutLayer(title = "Synthesis / 3 contexts - Tax income per households, 2013", south = FALSE, extent = com.spdf) +# Colours Typology and legend layout +colours <- c("#f0f0f0", "#fdc785","#ffffab","#fba9b0", + "#addea6","#ffa100","#fff226","#e30020") + # Plot typology map -typoLayer(spdf = com.spdf, df = mst, var = "colours", +typoLayer(spdf = com.spdf, df = mst, var = "mst", add = TRUE, border = "#D9D9D9", lwd=0.25, - col = levels(mst$colours), + col = colours, + legend.values.order = 0:7, legend.pos = "n") # Plot territorial level plot(ept.spdf,border="#252525",lwd=0.5,add=T) # Plot the legend -# Colours Typology and legend layout -colours <- c("#f0f0f0", "#fdc785","#ffffab","#fba9b0", - "#addea6","#ffa100","#fff226","#e30020") - rVal<-c(" . . . ","[X] . . ", " . [X] . ","[X] [X] . ", " . . [X]","[X] . [X]", @@ -1018,16 +1003,16 @@ Most of the communes of Seine-Saint-Denis and Val-de-Marne are characterised by 4 communes are characterised by low indexes in a local context, but not in a global one. It corresponds to the communes located in the immediate neighourhood of the wealthiest communes of the MGP (classes 4 and 6, in green and yellow): Paris 15e arrondissement, Suresnes, Puteaux and in a less extent Malakoff. -````{r synthesis80_class7, fig.width=7, fig.height=5, warning = FALSE, cache = TRUE, eval = FALSE} +````{r synthesis80_class7, fig.width=7, fig.height=5, warning = FALSE, cache = TRUE, eval = TRUE} # Communes in lagging situation for the three contexts -subset(mst, mst == 7, select = c(gdevrel, mdevrel, ldevrel, mst)) +subset(mst, mst == 7, select = c(gdevrel, tdevrel, sdevrel, mst)) -# Communes in favorable situation in a global context or in a global and in an territorial contexts -subset(mst, mst == 1 | mst == 3, select = c(gdevrel, mdevrel, ldevrel, mst)) +# Communes in lagging situation in a global context or in a global and in an territorial contexts +subset(mst, mst == 1 | mst == 3, select = c(gdevrel, tdevrel, sdevrel, mst)) -# Communes in favorable situation in a spatial context or in a spatial and a territorial context -subset(mst, mst == 4 | mst == 6, select = c(gdevrel, mdevrel, ldevrel, mst)) +# Communes in lagging situation in a spatial context or in a spatial and a territorial context +subset(mst, mst == 4 | mst == 6, select = c(gdevrel, tdevrel, sdevrel, mst)) ```` @@ -1035,12 +1020,12 @@ subset(mst, mst == 4 | mst == 6, select = c(gdevrel, mdevrel, ldevrel, mst)) The synthesisAbs function takes in entry all requested parameters to compute the three deviations, as specified above. It returns a dataframe summarizing the values of absolute deviations, e.g. how much should be retributed from the poorest to the wealthiest territorial units to ensure a perfect equilibrium of the ratio for the three contexts. Results are expressed both in absolute values (mass of numerator, amount of tax reference in Euros in this case) and as a share of the numerator (x % of the numerator that should be redistributed). -````{r synthesisabs, fig.width=7, fig.height=5, warning = FALSE, cache = TRUE, eval = FALSE} +````{r synthesisabs, fig.width=7, fig.height=5, warning = FALSE, cache = TRUE, eval = TRUE} # Compute the synthesis DataFrame (absolute deviations) mas(spdf = com.spdf, x = com, spdfid = "DEPCOM", xid = "DEPCOM", - var1 = "INC", var2 = "TH", dist = NULL, key = "EPT", order = 1, - mat = NULL) + var1 = "INC", var2 = "TH", dist = NULL, key = "EPT", order = 1, + mat = NULL) ```` For the MGP area, it is 22 billion Euros that should be redistributed from the communes in favorable situation to the communes in lagging situation. It corresponds to 16.3 % of the total mass of income declared to the taxes. If a policy option consists in ensuring a reequilibrium at an intermediate territorial level, such as the Etablissements Publics Territoriaux, it is 15 billion Euros that should be transfered (11.6 % of the income mass). If a solution chosen consists in limiting territorial discontinuities in a local context (avoid local poles of wealth or of poverty), it is 10 billion Euros that should be redistribued (7.9 % of the income mass). Ensuring an equilibrium of income in these three territorial contexts are obvioulsy not credible policy options, but it gives some references to monitor the magnitude of territorial inequalities existing in a given study area. @@ -1062,5 +1047,3 @@ That being said, MTA analysis provide a useful methodological base to explore se * Simulation of policy options: it is quite frequent that indicators constitute a basis for implementing policy measures to reduce territorial disparities. The case of the regional policy of the EU is a good example taking into account that most of the funding goes to regions below the statistical threshold of 75 % of the average of the European Union for the GDP per capita criteria at NUTS2 level. The functionalities of the MTA package allow in that perspective to simulate quickly the consequences of the use of several alternatives for guiding the funds allocation. * Highlight contradictions: the fact that MTA functionalities are based on 3 possible measures of territorial inequalities (general, territorial, spatial deviations) leads the analyst or the policy maker to think about several theories regarding the governance of territorial inequalities. A territorial unit situated in a lagging situation at general level and in a favourable situation at local level must not be considered in the same way than a territorial unit characterised by a lagging situation both at general and local levels. - -