{"id":432,"date":"2026-04-09T15:57:05","date_gmt":"2026-04-09T13:57:05","guid":{"rendered":"https:\/\/hainautstat.be\/news\/?page_id=432"},"modified":"2026-05-13T13:10:45","modified_gmt":"2026-05-13T11:10:45","slug":"methodologie","status":"publish","type":"page","link":"https:\/\/hainautstat.be\/news\/methodologie\/","title":{"rendered":"M\u00e9thodologie d\u2019\u00e9laboration des profils synth\u00e9tiques communaux"},"content":{"rendered":"\n

\ud83d\udd0e<\/strong> 1. Objectif de l\u2019analyse<\/strong><\/h2>\n\n\n\n

Le profil synth\u00e9tique de chaque commune hainuy\u00e8re est construit \u00e0 partir d\u2019un ensemble d\u2019indicateurs issus des profils communaux disponibles sur Hainaut Stat.<\/p>\n\n\n\n

L\u2019objectif est triple :<\/p>\n\n\n\n

    \n
  • Identifier les dimensions latentes structurantes<\/strong> caract\u00e9risant les communes<\/li>\n\n\n\n
  • S\u00e9lectionner les indicateurs les plus discriminants<\/strong> au sein de chaque dimension<\/li>\n\n\n\n
  • Positionner chaque commune<\/strong> par rapport \u00e0 la distribution observ\u00e9e \u00e0 l\u2019\u00e9chelle provinciale<\/li>\n<\/ul>\n\n\n\n
    <\/div>\n\n\n\n

    \u2699\ufe0f<\/strong> 2. Approche m\u00e9thodologique globale<\/strong><\/h2>\n\n\n\n

    La d\u00e9marche repose sur une analyse multivari\u00e9e exploratoire<\/strong>, articul\u00e9e autour d’ :<\/p>\n\n\n\n

      \n
    • Une r\u00e9duction dimensionnelle<\/strong> via une ACP<\/li>\n\n\n\n
    • Une s\u00e9lection d\u2019indicateurs pertinents<\/strong><\/li>\n\n\n\n
    • Une standardisation statistique<\/strong><\/li>\n\n\n\n
    • Une construction de scores synth\u00e9tiques<\/strong><\/li>\n\n\n\n
    • Une discr\u00e9tisation interpr\u00e9tative<\/strong><\/li>\n<\/ul>\n\n\n\n
      <\/div>\n\n\n\n

      \ud83d\udcca<\/strong> 3. Analyse en Composantes Principales (ACP)<\/strong><\/h2>\n\n\n\n

      \ud83d\udd2c<\/strong> Principe<\/strong><\/p>\n\n\n\n

      Une ACP<\/strong> (Analyse en Composantes Principales) est utilis\u00e9e afin de :<\/p>\n\n\n\n

        \n
      • R\u00e9duire la dimensionnalit\u00e9 des donn\u00e9es<\/li>\n\n\n\n
      • Identifier des axes factoriels orthogonaux<\/strong> maximisant la variance expliqu\u00e9e<\/li>\n\n\n\n
      • Mettre en \u00e9vidence des structures latentes<\/strong><\/li>\n<\/ul>\n\n\n\n

        \u2699\ufe0f<\/strong> Impl\u00e9mentation technique<\/strong><\/p>\n\n\n\n

          \n
        • Donn\u00e9es : variables quantitatives de type taux<\/strong> et ratios<\/strong><\/li>\n\n\n\n
        • Logiciel : R<\/li>\n\n\n\n
        • Packages utilis\u00e9s :\n
            \n
          • FactoMineR \u2192 calcul de l\u2019ACP<\/li>\n\n\n\n
          • factoextra \u2192 visualisation<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n

            Dimensions identifi\u00e9es<\/strong><\/p>\n\n\n\n

            L\u2019ACP a permis d\u2019identifier 5 dimensions structurantes<\/strong>, interpr\u00e9t\u00e9es comme des axes continus<\/strong> :<\/p>\n\n\n\n

            Dimension<\/strong><\/td>Interpr\u00e9tation<\/strong><\/td><\/tr><\/thead>
            Niveau de vie<\/td>De la pr\u00e9carit\u00e9 \u00e0 l\u2019aisance socio-\u00e9conomique<\/td><\/tr>
            Urbanisation<\/td>Du rural au fortement urbanis\u00e9<\/td><\/tr>
            Dynamisme d\u00e9mographique<\/td>Du d\u00e9clin \u00e0 l\u2019attractivit\u00e9 r\u00e9sidentielle<\/td><\/tr>
            Vieillissement<\/td>Des populations jeunes aux populations \u00e2g\u00e9es<\/td><\/tr>
            Attractivit\u00e9 \u00e9conomique<\/td>Faible polarisation \u2192 forte concentration d\u2019emplois<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
            \n\n\n\n
            <\/div>\n\n\n\n

            \ud83d\udd3d<\/strong> 4. S\u00e9lection des variables (r\u00e9duction dimensionnelle)<\/strong><\/h2>\n\n\n\n

            \ud83c\udfaf<\/strong> Crit\u00e8re retenu<\/strong><\/p>\n\n\n\n

            Les variables ont \u00e9t\u00e9 s\u00e9lectionn\u00e9es sur base de leur corr\u00e9lation avec les axes factoriels<\/strong> (charges factorielles) :<\/p>\n\n\n\n

              \n
            • Seuil retenu : |r| \u2265 0,5<\/strong><\/li>\n\n\n\n
            • Objectif : conserver uniquement les variables fortement contributives<\/strong><\/li>\n<\/ul>\n\n\n\n

              \ud83d\udccc<\/strong> Variables retenues par dimension<\/strong><\/p>\n\n\n\n

               Niveau de vie<\/strong><\/p>\n\n\n\n

              Indicateur<\/strong><\/td>Corr\u00e9lation<\/strong><\/td><\/tr><\/thead>
              Revenu annuel m\u00e9dian par d\u00e9claration (\u20ac)<\/td>-0,87<\/td><\/tr>
              Niveau d’Instruction \u00e9lev\u00e9e (%)<\/td>-0,81<\/td><\/tr>
              Niveau d’Instruction faible (%)<\/td>0,87<\/td><\/tr>
              Part des mineurs dont parents sans revenu du travail (%)<\/td>0,91<\/td><\/tr>
              Part des b\u00e9n\u00e9ficiaires RIS parmi les 18-64 ans (%)<\/td>0,91<\/td><\/tr>
              Taux de ch\u00f4mage (%)<\/td>0,85<\/td><\/tr>
              Part des b\u00e9n\u00e9ficiaires de la GRAPA (%)<\/td>0,82<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

              Urbanisation<\/strong><\/p>\n\n\n\n

              Indicateur<\/strong><\/td>Corr\u00e9lation<\/strong><\/td><\/tr><\/thead>
              Taux d’offre fonci\u00e8re disponible en zone d’habitat (%)<\/td>-0,64<\/td><\/tr>
              Densit\u00e9 de population (habitants\/km\u00b2)<\/td>0,82<\/td><\/tr>
              Part des terrains artificialis\u00e9s (%)<\/td>0,78<\/td><\/tr>
              Part des familles monoparentales (%)<\/td>0,80<\/td><\/tr>
              Surface r\u00e9sidentielle par habitant (m\u00b2\/hab.)<\/td>-0,86<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

              Dynamisme d\u00e9mographique<\/strong><\/p>\n\n\n\n

              Indicateur<\/strong><\/td>Corr\u00e9lation<\/strong><\/td><\/tr><\/thead>
              Taux de croissance d\u00e9cennale des 18\u201364 ans (%)<\/td>0,83<\/td><\/tr>
              Taux d’accroissement migratoire annuel moyen (%)<\/td>0,70<\/td><\/tr>
              Taux de croissance d\u00e9cennale de la population (%)<\/td>0,83<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

              Vieillissement<\/strong><\/p>\n\n\n\n

              Indicateur<\/strong><\/td>Corr\u00e9lation<\/strong><\/td><\/tr><\/thead>
              Indice de vieillissement<\/td>0,59<\/td><\/tr>
              Taux brut de natalit\u00e9 (%)<\/td>-0,51<\/td><\/tr>
              Taux de croissance d\u00e9cennale des plus de 65 ans (%)<\/td>0,58<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

              Attractivit\u00e9 \u00e9conomique<\/strong><\/p>\n\n\n\n

              Indicateur<\/strong><\/td>Corr\u00e9lation<\/strong><\/td><\/tr><\/thead>
              Part de la mobilit\u00e9 entrante (%)<\/td>0,50<\/td><\/tr>
              Ratio d\u2019emploi int\u00e9rieur<\/td>0,79<\/td><\/tr>
              Ratio d\u2019attractivit\u00e9<\/td>0,79<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

              \u26a0\ufe0f<\/strong> Remarque m\u00e9thodologique importante<\/strong><\/p>\n\n\n\n

                \n
              • Certaines variables pr\u00e9sentent des corr\u00e9lations n\u00e9gatives<\/strong><\/li>\n\n\n\n
              • Afin d\u2019assurer une coh\u00e9rence interpr\u00e9tative<\/strong>, les signes ont \u00e9t\u00e9 harmonis\u00e9s<\/strong>
                \ud83d\udc49 Exemple : inversion pour que des valeurs \u00e9lev\u00e9es traduisent syst\u00e9matiquement une situation favorable (ex : niveau de vie \u00e9lev\u00e9)<\/li>\n<\/ul>\n\n\n\n
                <\/div>\n\n\n\n

                \ud83d\udccf<\/strong> 5. Standardisation des variables (Z-score)<\/strong><\/h2>\n\n\n\n

                \ud83d\udd2c<\/strong> Objectif<\/strong><\/p>\n\n\n\n

                Rendre les variables :<\/p>\n\n\n\n

                  \n
                • comparables<\/li>\n\n\n\n
                • centr\u00e9es<\/li>\n\n\n\n
                • ind\u00e9pendantes de leur unit\u00e9<\/li>\n<\/ul>\n\n\n\n

                  \ud83d\udcd0<\/strong> Formule<\/strong><\/p>\n\n\n\n

                  z=\u03c3x\u2212\u03bc\u200b<\/p>\n\n\n\n

                  \ud83d\udcca<\/strong> Interpr\u00e9tation<\/strong><\/p>\n\n\n\n

                  Z-score<\/strong><\/td>Signification<\/strong><\/td><\/tr><\/thead>
                  0<\/td>Moyenne<\/td><\/tr>
                  +1<\/td>1 \u00e9cart-type au-dessus<\/td><\/tr>
                  -1<\/td>1 \u00e9cart-type en dessous<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

                  \u26a0\ufe0f<\/strong> Pr\u00e9cision m\u00e9thodologique<\/strong><\/p>\n\n\n\n

                  La standardisation est r\u00e9alis\u00e9e :<\/p>\n\n\n\n

                    \n
                  • variable par variable<\/strong> (et non sur la somme)<\/li>\n\n\n\n
                  • \u00e0 partir de la distribution observ\u00e9e \u00e0 l\u2019\u00e9chelle provinciale<\/strong><\/li>\n<\/ul>\n\n\n\n
                    <\/div>\n\n\n\n

                    \ud83d\udcc8<\/strong> 6. Construction des scores synth\u00e9tiques<\/strong><\/h2>\n\n\n\n

                    M\u00e9thode<\/strong><\/p>\n\n\n\n

                    Pour chaque dimension :<\/p>\n\n\n\n

                    1\/n\u200b\u2211z<\/p>\n\n\n\n

                    \ud83d\udc49 Moyenne des Z-scores des variables associ\u00e9es<\/p>\n\n\n\n

                    \ud83c\udfaf<\/strong> Interpr\u00e9tation<\/strong><\/p>\n\n\n\n

                      \n
                    • Score > 0 \u2192 commune au-dessus de la moyenne provinciale<\/li>\n\n\n\n
                    • Score < 0 \u2192 commune en dessous<\/li>\n<\/ul>\n\n\n\n
                      \n\n\n\n
                      <\/div>\n\n\n\n

                      7. Discr\u00e9tisation des scores<\/strong><\/h2>\n\n\n\n

                      Afin de faciliter la lecture, les scores continus sont transform\u00e9s en classes ordinales<\/strong> :<\/p>\n\n\n\n

                      Intervalle Z-score<\/strong><\/td>Cat\u00e9gorie<\/strong><\/td>Interpr\u00e9tation<\/strong><\/td><\/tr><\/thead>
                      < -1,5<\/td>Tr\u00e8s faible<\/td>Forte sous-performance<\/td><\/tr>
                      [-1,5 ; -0,5]<\/td>Faible<\/td>Sous la moyenne<\/td><\/tr>
                      [-0,5 ; 0,5]<\/td>Moyen<\/td>Proche de la moyenne<\/td><\/tr>
                      [0,5 ; 1,5]<\/td>\u00c9lev\u00e9<\/td>Au-dessus de la moyenne<\/td><\/tr>
                      > 1,5<\/td>Tr\u00e8s \u00e9lev\u00e9<\/td>Forte surperformance<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

                      <\/p>\n","protected":false},"excerpt":{"rendered":"

                      \ud83d\udd0e 1. Objectif de l\u2019analyse Le profil synth\u00e9tique de chaque commune hainuy\u00e8re est construit \u00e0 partir d\u2019un ensemble d\u2019indicateurs issus des profils communaux disponibles sur Hainaut Stat. L\u2019objectif est triple : \u2699\ufe0f 2. Approche m\u00e9thodologique globale La d\u00e9marche repose sur une analyse multivari\u00e9e exploratoire, articul\u00e9e autour d’ : \ud83d\udcca 3. Analyse en Composantes Principales (ACP) […]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-432","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"\nM\u00e9thodologie d\u2019\u00e9laboration des profils synth\u00e9tiques communaux - Hainaut Stat<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/hainautstat.be\/news\/methodologie\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"M\u00e9thodologie d\u2019\u00e9laboration des profils synth\u00e9tiques communaux - Hainaut Stat\" \/>\n<meta property=\"og:description\" content=\"\ud83d\udd0e 1. Objectif de l\u2019analyse Le profil synth\u00e9tique de chaque commune hainuy\u00e8re est construit \u00e0 partir d\u2019un ensemble d\u2019indicateurs issus des profils communaux disponibles sur Hainaut Stat. L\u2019objectif est triple : \u2699\ufe0f 2. Approche m\u00e9thodologique globale La d\u00e9marche repose sur une analyse multivari\u00e9e exploratoire, articul\u00e9e autour d’ : \ud83d\udcca 3. 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