El objetivo de este informe es desarrollar el analisis que permita el Analisis de la población centenaria en Colombia a partir del Censo Nacional de Población y Vivienda de 2018.
Se ha plateado el analisis en los siguientes componentes:
Analisis descriptivo de la población a nivel de hogares con Población centenaria
Se calcularon las tasas de población centenaria: \[TCE_i= \sum(cent_i) / \sum(pob_i)\]
En el presente analisis se toman en cuenta 1119 poligonos correspondientes a la totalidad de municipio y las areas no municipalizadas continentales. L anterior dado que el analisis de datos de area se basa en las vecindades entre poligonos.
summary(cars)
## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00
## Warning: st_crs<- : replacing crs does not reproject data; use st_transform for
## that
MPIO_CDPMP | DPTO_CNMBR | MPIO_CNMBR | P | CE | CE_G1 | CE_G2 | CE_G3 | TCE |
---|---|---|---|---|---|---|---|---|
66383 | RISARALDA | LA CELIA | 6178 | 45 | 0 | 45 | 0 | 72.84 |
5101 | ANTIOQUIA | CIUDAD BOLÍVAR | 23361 | 132 | 5 | 127 | 0 | 56.50 |
47258 | MAGDALENA | EL PIÑÓN | 17308 | 58 | 4 | 54 | 0 | 33.51 |
15861 | BOYACÁ | VENTAQUEMADA | 13984 | 37 | 2 | 34 | 1 | 26.46 |
91460 | AMAZONAS | MIRITÍ - PARANÁ | 1023 | 2 | 0 | 1 | 1 | 19.55 |
85315 | CASANARE | SÁCAMA | 1675 | 3 | 3 | 0 | 0 | 17.91 |
15778 | BOYACÁ | SUTATENZA | 3501 | 6 | 6 | 0 | 0 | 17.14 |
76403 | VALLE DEL CAUCA | LA VICTORIA | 11058 | 18 | 14 | 4 | 0 | 16.28 |
27425 | CHOCÓ | MEDIO ATRATO | 10140 | 15 | 7 | 5 | 3 | 14.79 |
15248 | BOYACÁ | EL ESPINO | 2715 | 4 | 4 | 0 | 0 | 14.73 |
15425 | BOYACÁ | MACANAL | 3568 | 5 | 2 | 1 | 2 | 14.01 |
15090 | BOYACÁ | BERBEO | 1453 | 2 | 2 | 0 | 0 | 13.76 |
15660 | BOYACÁ | SAN EDUARDO | 1572 | 2 | 2 | 0 | 0 | 12.72 |
15522 | BOYACÁ | PANQUEBA | 1644 | 2 | 0 | 0 | 2 | 12.17 |
94884 | GUAINÍA | PUERTO COLOMBIA | 1643 | 2 | 1 | 0 | 1 | 12.17 |
25168 | CUNDINAMARCA | CHAGUANÍ | 3323 | 4 | 2 | 0 | 2 | 12.04 |
13212 | BOLÍVAR | CÓRDOBA | 15012 | 17 | 3 | 13 | 1 | 11.32 |
13442 | BOLÍVAR | MARÍA LA BAJA | 45324 | 51 | 22 | 11 | 18 | 11.25 |
27745 | CHOCÓ | SIPÍ | 2768 | 3 | 1 | 2 | 0 | 10.84 |
15092 | BOYACÁ | BETÉITIVA | 1895 | 2 | 2 | 0 | 0 | 10.55 |
15403 | BOYACÁ | LA UVITA | 2872 | 3 | 3 | 0 | 0 | 10.45 |
85015 | CASANARE | CHÁMEZA | 2009 | 2 | 2 | 0 | 0 | 9.96 |
52233 | NARIÑO | CUMBITARA | 5096 | 5 | 3 | 0 | 2 | 9.81 |
15232 | BOYACÁ | CHÍQUIZA | 4347 | 4 | 1 | 2 | 1 | 9.20 |
68211 | SANTANDER | CONTRATACIÓN | 3296 | 3 | 2 | 0 | 1 | 9.10 |
## Warning in getSpPPolygonsLabptSlots(colpeso.utm): use coordinates method
## Warning in CPL_crs_from_input(x): GDAL Message 1: +init=epsg:XXXX syntax is
## deprecated. It might return a CRS with a non-EPSG compliant axis order.
##
## Moran I test under randomisation
##
## data: colpeso$TCE
## weights: colpeso.lw
##
## Moran I statistic standard deviate = 2.2933, p-value = 0.01091
## alternative hypothesis: greater
## sample estimates:
## Moran I statistic Expectation Variance
## 0.0372529835 -0.0008944544 0.0002766956
##
## Monte-Carlo simulation of Moran I
##
## data: colpeso$TCE
## weights: colpeso.lwb
## number of simulations + 1: 1001
##
## statistic = 0.03378, observed rank = 970, p-value = 0.03097
## alternative hypothesis: greater
##
## Geary C test under randomisation
##
## data: colpeso$TCE
## weights: colpeso.lw
##
## Geary C statistic standard deviate = 1.6794, p-value = 0.04654
## alternative hypothesis: Expectation greater than statistic
## sample estimates:
## Geary C statistic Expectation Variance
## 0.873551554 1.000000000 0.005669266
## Spatial correlogram for colpeso.utm$TCE
## method: Moran's I
## estimate expectation variance standard deviate Pr(I) two sided
## 1 (1119) 3.7253e-02 -8.9445e-04 3.2194e-04 2.1261 0.03350 *
## 2 (1119) 7.5012e-03 -8.9445e-04 1.3311e-04 0.7277 0.46680
## 3 (1119) 1.2934e-02 -8.9445e-04 7.6728e-05 1.5787 0.11441
## 4 (1119) 1.7489e-02 -8.9445e-04 5.2954e-05 2.5262 0.01153 *
## 5 (1119) 1.3951e-02 -8.9445e-04 4.0131e-05 2.3435 0.01910 *
## 6 (1119) -4.2911e-03 -8.9445e-04 3.2260e-05 -0.5980 0.54983
## 7 (1119) -3.1539e-03 -8.9445e-04 2.7233e-05 -0.4330 0.66504
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Spatial correlogram for colpeso.utm$TCE
## method: Geary's C
## estimate expectation variance standard deviate Pr(I) two sided
## 1 (1119) 8.7355e-01 1.0000e+00 3.8919e-04 -6.4097 1.459e-10 ***
## 2 (1119) 9.1476e-01 1.0000e+00 1.8116e-04 -6.3331 2.403e-10 ***
## 3 (1119) 9.8581e-01 1.0000e+00 1.1678e-04 -1.3128 0.189245
## 4 (1119) 9.6289e-01 1.0000e+00 9.4227e-05 -3.8226 0.000132 ***
## 5 (1119) 9.4576e-01 1.0000e+00 8.2463e-05 -5.9729 2.331e-09 ***
## 6 (1119) 9.5737e-01 1.0000e+00 7.6680e-05 -4.8678 1.129e-06 ***
## 7 (1119) 9.3328e-01 1.0000e+00 7.5865e-05 -7.6603 1.855e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Simple feature collection with 19 features and 2 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -77.15989 ymin: -2.003434 xmax: -69.3955 ymax: 10.6781
## Projected CRS: +proj=tmerc +lat_0=4.599047222222222 +lon_0=-74.08091666666667 +k=1 +x_0=1000000 +y_0=1000000 +ellps=intl +towgs84=307,304,-318,0,0,0,0 +units=m +no_defs
## First 10 features:
## key MPIO_CNMBR geometry
## 58 5353 HISPANIA MULTIPOLYGON (((-75.90035 5...
## 214 15183 CHITA MULTIPOLYGON (((-72.46477 6...
## 234 15299 GARAGOA MULTIPOLYGON (((-73.26632 5...
## 256 15494 NUEVO COLÓN MULTIPOLYGON (((-73.4359 5....
## 280 15673 SAN MATEO MULTIPOLYGON (((-72.54832 6...
## 294 15761 SOMONDOCO MULTIPOLYGON (((-73.37998 5...
## 302 15798 TENZA MULTIPOLYGON (((-73.41786 5...
## 496 25317 GUACHETÁ MULTIPOLYGON (((-73.67656 5...
## 575 27001 QUIBDÓ MULTIPOLYGON (((-76.79793 6...
## 587 27245 EL CARMEN DE ATRATO MULTIPOLYGON (((-76.11068 6...