Tugas Week 4 - Statistika Lingkungan || Statistika Deskriptif dan Visualisasi Data Lingkungan
Nurdiana Rizki R
2025-09-23
library (ggplot2)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(sf)## Linking to GEOS 3.13.1, GDAL 3.11.0, PROJ 9.6.0; sf_use_s2() is TRUEMembaca shapefile (Provinsi Jawa Tengah)
peta <- st_read("D:/Semester 5/Statistika Lingkungan/33.24_kecamatan.geojson")## Reading layer `33.24_kecamatan' from data source
## `D:\Semester 5\Statistika Lingkungan\33.24_kecamatan.geojson'
## using driver `GeoJSON'
## Simple feature collection with 20 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension: XYZ
## Bounding box: xmin: 109.9227 ymin: -7.201756 xmax: 110.3664 ymax: -6.833059
## z_range: zmin: 0 zmax: 0
## Geodetic CRS: WGS 84peta## Simple feature collection with 20 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension: XYZ
## Bounding box: xmin: 109.9227 ymin: -7.201756 xmax: 110.3664 ymax: -6.833059
## z_range: zmin: 0 zmax: 0
## Geodetic CRS: WGS 84
## First 10 features:
## kd_propinsi kd_dati2 kd_kecamatan nm_kecamatan
## 1 33 24 001 Plantungan
## 2 33 24 002 Pageruyung
## 3 33 24 003 Sukorejo
## 4 33 24 004 Patean
## 5 33 24 005 Singorojo
## 6 33 24 006 Limbangan
## 7 33 24 007 Boja
## 8 33 24 008 Kaliwungu
## 9 33 24 009 Brangsong
## 10 33 24 010 Pegandon
## geometry
## 1 MULTIPOLYGON Z (((109.9743 ...
## 2 MULTIPOLYGON Z (((110.0187 ...
## 3 MULTIPOLYGON Z (((109.9986 ...
## 4 MULTIPOLYGON Z (((110.0589 ...
## 5 MULTIPOLYGON Z (((110.2228 ...
## 6 MULTIPOLYGON Z (((110.2844 ...
## 7 MULTIPOLYGON Z (((110.2865 ...
## 8 MULTIPOLYGON Z (((110.2845 ...
## 9 MULTIPOLYGON Z (((110.2086 ...
## 10 MULTIPOLYGON Z (((110.1381 ...Simulasi data Rawan Banjir untuk tiap kecamatan di Kota Kendal
set.seed(123)
data_banjir <- data.frame(
Kecamatan = peta$nm_kecamatan,
Lvl_Banjir = sample(1:10, length(peta$nm_kecamatan), replace = TRUE) #1 rendah - 10 sangat rawan
)
data_banjir## Kecamatan Lvl_Banjir
## 1 Plantungan 3
## 2 Pageruyung 3
## 3 Sukorejo 10
## 4 Patean 2
## 5 Singorojo 6
## 6 Limbangan 5
## 7 Boja 4
## 8 Kaliwungu 6
## 9 Brangsong 9
## 10 Pegandon 10
## 11 Gemuh 5
## 12 Weleri 3
## 13 Cepiring 9
## 14 Patebon 9
## 15 Kendal 9
## 16 Rowosari 3
## 17 Kangkung 8
## 18 Ringinarum 10
## 19 Ngampel 7
## 20 Kaliwungu Selatan 10Menggabungkan data dengan peta
peta_data <- left_join(peta, data_banjir, by=c("nm_kecamatan"="Kecamatan"))
peta_data## Simple feature collection with 20 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension: XYZ
## Bounding box: xmin: 109.9227 ymin: -7.201756 xmax: 110.3664 ymax: -6.833059
## z_range: zmin: 0 zmax: 0
## Geodetic CRS: WGS 84
## First 10 features:
## kd_propinsi kd_dati2 kd_kecamatan nm_kecamatan Lvl_Banjir
## 1 33 24 001 Plantungan 3
## 2 33 24 002 Pageruyung 3
## 3 33 24 003 Sukorejo 10
## 4 33 24 004 Patean 2
## 5 33 24 005 Singorojo 6
## 6 33 24 006 Limbangan 5
## 7 33 24 007 Boja 4
## 8 33 24 008 Kaliwungu 6
## 9 33 24 009 Brangsong 9
## 10 33 24 010 Pegandon 10
## geometry
## 1 MULTIPOLYGON Z (((109.9743 ...
## 2 MULTIPOLYGON Z (((110.0187 ...
## 3 MULTIPOLYGON Z (((109.9986 ...
## 4 MULTIPOLYGON Z (((110.0589 ...
## 5 MULTIPOLYGON Z (((110.2228 ...
## 6 MULTIPOLYGON Z (((110.2844 ...
## 7 MULTIPOLYGON Z (((110.2865 ...
## 8 MULTIPOLYGON Z (((110.2845 ...
## 9 MULTIPOLYGON Z (((110.2086 ...
## 10 MULTIPOLYGON Z (((110.1381 ...2.Plot Choropleth
ggplot(peta_data) +
geom_sf(aes(fill=Lvl_Banjir), color="white") +
geom_sf_text(aes(label = nm_kecamatan), size = 2, color = "white") +scale_fill_gradient(low="burlywood", high="brown", name="Lvl_Banjir") +theme_minimal() +
labs(title="Choropleth Map Lvl Banjir - Kota Kendal")## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data
# buat titik density: setiap nilai Lvl_Banjir
library(sf)
library(ggplot2)
library(dplyr)
# generate titik sesuai Lvl_Banjir
dots <- peta_data %>%
st_cast("MULTIPOLYGON") %>%
st_sample(size = .$Lvl_Banjir, type = "random") %>%
st_sf() %>%
mutate(value = rep(peta_data$Lvl_Banjir, peta_data$Lvl_Banjir))## Warning in st_poly_sample(x, size = size, ..., type = type, by_polygon =
## by_polygon, : coordinate ranges not computed along great circles; install
## package lwgeom to get rid of this warning## st_as_s2(): dropping Z and/or M coordinate
## st_as_s2(): dropping Z and/or M coordinate## Warning in st_poly_sample(x, size = size, ..., type = type, by_polygon =
## by_polygon, : coordinate ranges not computed along great circles; install
## package lwgeom to get rid of this warning## st_as_s2(): dropping Z and/or M coordinate
## st_as_s2(): dropping Z and/or M coordinate## Warning in st_poly_sample(x, size = size, ..., type = type, by_polygon =
## by_polygon, : coordinate ranges not computed along great circles; install
## package lwgeom to get rid of this warning## st_as_s2(): dropping Z and/or M coordinate
## st_as_s2(): dropping Z and/or M coordinate## Warning in st_poly_sample(x, size = size, ..., type = type, by_polygon =
## by_polygon, : coordinate ranges not computed along great circles; install
## package lwgeom to get rid of this warning## st_as_s2(): dropping Z and/or M coordinate
## st_as_s2(): dropping Z and/or M coordinate## Warning in st_poly_sample(x, size = size, ..., type = type, by_polygon =
## by_polygon, : coordinate ranges not computed along great circles; install
## package lwgeom to get rid of this warning## st_as_s2(): dropping Z and/or M coordinate
## st_as_s2(): dropping Z and/or M coordinate## Warning in st_poly_sample(x, size = size, ..., type = type, by_polygon =
## by_polygon, : coordinate ranges not computed along great circles; install
## package lwgeom to get rid of this warning## st_as_s2(): dropping Z and/or M coordinate
## st_as_s2(): dropping Z and/or M coordinate## Warning in st_poly_sample(x, size = size, ..., type = type, by_polygon =
## by_polygon, : coordinate ranges not computed along great circles; install
## package lwgeom to get rid of this warning## st_as_s2(): dropping Z and/or M coordinate
## st_as_s2(): dropping Z and/or M coordinate## Warning in st_poly_sample(x, size = size, ..., type = type, by_polygon =
## by_polygon, : coordinate ranges not computed along great circles; install
## package lwgeom to get rid of this warning## st_as_s2(): dropping Z and/or M coordinate
## st_as_s2(): dropping Z and/or M coordinate## Warning in st_poly_sample(x, size = size, ..., type = type, by_polygon =
## by_polygon, : coordinate ranges not computed along great circles; install
## package lwgeom to get rid of this warning## st_as_s2(): dropping Z and/or M coordinate
## st_as_s2(): dropping Z and/or M coordinate## Warning in st_poly_sample(x, size = size, ..., type = type, by_polygon =
## by_polygon, : coordinate ranges not computed along great circles; install
## package lwgeom to get rid of this warning## st_as_s2(): dropping Z and/or M coordinate
## st_as_s2(): dropping Z and/or M coordinate## Warning in st_poly_sample(x, size = size, ..., type = type, by_polygon =
## by_polygon, : coordinate ranges not computed along great circles; install
## package lwgeom to get rid of this warning## st_as_s2(): dropping Z and/or M coordinate
## st_as_s2(): dropping Z and/or M coordinate## Warning in st_poly_sample(x, size = size, ..., type = type, by_polygon =
## by_polygon, : coordinate ranges not computed along great circles; install
## package lwgeom to get rid of this warning## st_as_s2(): dropping Z and/or M coordinate
## st_as_s2(): dropping Z and/or M coordinate## Warning in st_poly_sample(x, size = size, ..., type = type, by_polygon =
## by_polygon, : coordinate ranges not computed along great circles; install
## package lwgeom to get rid of this warning## st_as_s2(): dropping Z and/or M coordinate
## st_as_s2(): dropping Z and/or M coordinate## Warning in st_poly_sample(x, size = size, ..., type = type, by_polygon =
## by_polygon, : coordinate ranges not computed along great circles; install
## package lwgeom to get rid of this warning## st_as_s2(): dropping Z and/or M coordinate
## st_as_s2(): dropping Z and/or M coordinate## Warning in st_poly_sample(x, size = size, ..., type = type, by_polygon =
## by_polygon, : coordinate ranges not computed along great circles; install
## package lwgeom to get rid of this warning## st_as_s2(): dropping Z and/or M coordinate
## st_as_s2(): dropping Z and/or M coordinate## Warning in st_poly_sample(x, size = size, ..., type = type, by_polygon =
## by_polygon, : coordinate ranges not computed along great circles; install
## package lwgeom to get rid of this warning## st_as_s2(): dropping Z and/or M coordinate
## st_as_s2(): dropping Z and/or M coordinate## Warning in st_poly_sample(x, size = size, ..., type = type, by_polygon =
## by_polygon, : coordinate ranges not computed along great circles; install
## package lwgeom to get rid of this warning## st_as_s2(): dropping Z and/or M coordinate
## st_as_s2(): dropping Z and/or M coordinate## Warning in st_poly_sample(x, size = size, ..., type = type, by_polygon =
## by_polygon, : coordinate ranges not computed along great circles; install
## package lwgeom to get rid of this warning## st_as_s2(): dropping Z and/or M coordinate
## st_as_s2(): dropping Z and/or M coordinate## Warning in st_poly_sample(x, size = size, ..., type = type, by_polygon =
## by_polygon, : coordinate ranges not computed along great circles; install
## package lwgeom to get rid of this warning## st_as_s2(): dropping Z and/or M coordinate
## st_as_s2(): dropping Z and/or M coordinate## Warning in st_poly_sample(x, size = size, ..., type = type, by_polygon =
## by_polygon, : coordinate ranges not computed along great circles; install
## package lwgeom to get rid of this warning## st_as_s2(): dropping Z and/or M coordinate
## st_as_s2(): dropping Z and/or M coordinate## Warning in st_poly_sample(x, size = size, ..., type = type, by_polygon =
## by_polygon, : coordinate ranges not computed along great circles; install
## package lwgeom to get rid of this warning## st_as_s2(): dropping Z and/or M coordinate
## st_as_s2(): dropping Z and/or M coordinate## Warning in st_poly_sample(x, size = size, ..., type = type, by_polygon =
## by_polygon, : coordinate ranges not computed along great circles; install
## package lwgeom to get rid of this warning## st_as_s2(): dropping Z and/or M coordinate
## st_as_s2(): dropping Z and/or M coordinate## Warning in st_poly_sample(x, size = size, ..., type = type, by_polygon =
## by_polygon, : coordinate ranges not computed along great circles; install
## package lwgeom to get rid of this warning## st_as_s2(): dropping Z and/or M coordinate
## st_as_s2(): dropping Z and/or M coordinate## Warning in st_poly_sample(x, size = size, ..., type = type, by_polygon =
## by_polygon, : coordinate ranges not computed along great circles; install
## package lwgeom to get rid of this warning## st_as_s2(): dropping Z and/or M coordinate
## st_as_s2(): dropping Z and/or M coordinate## Warning in st_poly_sample(x, size = size, ..., type = type, by_polygon =
## by_polygon, : coordinate ranges not computed along great circles; install
## package lwgeom to get rid of this warning## st_as_s2(): dropping Z and/or M coordinate
## st_as_s2(): dropping Z and/or M coordinate# mengambil centroid kecamatan untuk label
centroids <- st_centroid(peta_data)## Warning: st_centroid assumes attributes are constant over geometries## st_as_s2(): dropping Z and/or M coordinate# plot dot density + label
ggplot() +
geom_sf(data = peta_data, fill = "grey90", color = "white") +
geom_sf(data = dots, aes(color = value), size = 2, alpha = 0.8) +
geom_text(data = centroids %>% st_drop_geometry(),
aes(x = st_coordinates(centroids)[,1],
y = st_coordinates(centroids)[,2],
label = nm_kecamatan),
size = 3, color = "black", fontface = "bold") +
scale_color_gradient(low = "burlywood", high = "brown") +
theme_minimal() +
labs(title = "Dot Density Map Level Banjir per Kecamatan",
color = "Level Banjir")
Interpretasi: Warna gelap → level banjir tinggi. Warna terang → level
banjir rendah.
Insight: Hasil menunjukkan bahwa kecamatan di wilayah utara dan tengah seperti Weleri, Rowosari, Kangkung, Kendal, Brangsong, Kaliwungu, dan Kaliwungu Selatan memiliki level banjir lebih tinggi. Sementara itu, kecamatan di bagian selatan seperti Boja, Limbangan, Singorojo, Plantungan, dan Sukorejo cenderung memiliki level banjir lebih rendah. Secara umum, terlihat adanya perbedaan pola antara wilayah utara yang lebih rawan banjir dibanding wilayah selatan.
2.Plot line graph
simulasi perubahan level banjir berdasarkan bulan
library(dplyr)
set.seed(223)
# Bulan (menggunakan angka untuk mempermudah)
bulan <- 1:12
# Buat grid data
data_banjir <- data.frame(Bulan = bulan)
# Simulasi realistis
set.seed(123)
data_banjir <- data_banjir %>%
rowwise() %>%
mutate(Level_Banjir = {
base <- 3
# Musim hujan (Des-Feb, Maret) lebih tinggi
if (Bulan %in% c(12, 1, 2, 3)) base <- 8
# Peralihan (Apr-Mei, Okt-Nov) sedang
if (Bulan %in% c(4, 5, 10, 11)) base <- 5
# Musim kemarau (Jun-Sep) rendah
if (Bulan %in% c(6, 7, 8, 9)) base <- 2
# Variasi acak
round(rnorm(1, mean = base, sd = 1))
}) %>%
ungroup()
print(data_banjir)## # A tibble: 12 × 2
## Bulan Level_Banjir
## <int> <dbl>
## 1 1 7
## 2 2 8
## 3 3 10
## 4 4 5
## 5 5 5
## 6 6 4
## 7 7 2
## 8 8 1
## 9 9 1
## 10 10 5
## 11 11 6
## 12 12 8library(ggplot2)
ggplot(data_banjir, aes(x = Bulan, y = Level_Banjir)) +
geom_line(color = "pink", size = 1.2) +
geom_point(size = 3, color = "red") +
theme_minimal() +
labs(title = "Perubahan Level Banjir per Bulan",
x = "Bulan", y = "Level Banjir")## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Grafik menunjukkan bahwa level banjir berfluktuasi cukup tajam sepanjang
tahun. Pada awal tahun (Januari–Maret), level banjir relatif tinggi dan
mencapai puncaknya di bulan Maret dengan nilai tertinggi 10. Setelah
itu, terjadi penurunan drastis pada April hingga Juni, dan level banjir
mencapai titik terendah di bulan Agustus–September dengan nilai hanya
sekitar 1. Mulai bulan Oktober, tren kembali meningkat secara bertahap
hingga Desember dengan level mendekati 8.
simulasi Drainase vs. Persentase Wilayah Tergenang
library(ggplot2)
library(dplyr)
set.seed(123)
# Data simulasi
tahun <- 2021:2025
df <- data.frame(
Tahun = tahun,
Indeks_Drainase = round(runif(length(tahun), 40, 90),1), # kualitas drainase
Persen_Banjir = round(runif(length(tahun), 5, 40),1) # % wilayah tergenang
)
# Plot Connected Scatter Plot
ggplot(df, aes(x = Indeks_Drainase, y = Persen_Banjir)) +
geom_path(color = "red", size = 1.2) + # garis penghubung
geom_point(color = "red", size = 3) + # titik tiap tahun
geom_text(aes(label = Tahun), vjust = -1, size = 3.5) + # label tahun
theme_minimal() +
labs(
title = "Connected Scatter Plot: Drainase vs. Banjir",
x = "Indeks Drainase (0-100)",
y = "Persentase Wilayah Banjir (%)"
)
Grafik connected scatter plot antara indeks drainase dan persentase
wilayah banjir di Kabupaten Kendal menunjukkan bahwa hubungan keduanya
tidak selalu linier. Pada tahun 2021, indeks drainase masih relatif
rendah dan wilayah terdampak banjir juga kecil. Namun, pada 2022-2023
meskipun terjadi peningkatan kualitas drainase, persentase wilayah yang
mengalami banjir justru meningkat. Hal ini menggambarkan bahwa perbaikan
infrastruktur drainase belum serta-merta menurunkan banjir, karena
faktor lain seperti curah hujan ekstrem, kapasitas saluran yang tidak
merata, serta alih fungsi lahan tetap berkontribusi terhadap
meningkatnya genangan. Pada 2024–2025 terlihat tren penurunan persentase
wilayah banjir seiring dengan semakin tingginya indeks drainase.
Fenomena ini mengindikasikan adanya jeda waktu antara pembangunan
drainase dengan dampak nyata yang dirasakan di lapangan. Dengan
demikian, dapat ditarik kesimpulan bahwa pengendalian banjir di Kendal
tidak cukup hanya mengandalkan perbaikan drainase di satu titik atau
kecamatan saja, melainkan perlu sistem yang terintegrasi antar wilayah,
disertai pengendalian tata ruang dan mitigasi curah hujan ekstrem, agar
tren penurunan banjir dapat berkelanjutan.