中国地图各省份降雨量数据模拟与可视化

原创已于 2025-07-07 23:29:43 修改 · 752 阅读

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于 2025-07-07 23:28:04 首次发布

中国地图各省份降雨量数据模拟与可视化

概述

本文档展示如何使用R语言模拟中国各省份的降雨量数据，并在地图上进行可视化展示。

环境准备

安装和加载必要的R包

# 安装必要的包（如果未安装）
packages <- c("ggplot2", "sf", "dplyr", "viridis", "RColorBrewer",
              "mapdata", "maps", "readr", "scales")

for(pkg in packages) {
  if(!require(pkg, character.only = TRUE)) {
    install.packages(pkg)
    library(pkg, character.only = TRUE)
  }
}

# 如果需要中国地图数据，可能需要安装以下包
# install.packages("chinamap", repos = "https://cran.r-project.org/")
# 或者使用在线资源获取中国地图数据

数据准备

1. 创建中国省份列表和模拟降雨量数据

# 中国34个省级行政区（包括港澳台）
provinces <- c(
  "北京市", "天津市", "河北省", "山西省", "内蒙古自治区", "辽宁省", "吉林省", "黑龙江省",
  "上海市", "江苏省", "浙江省", "安徽省", "福建省", "江西省", "山东省", "河南省",
  "湖北省", "湖南省", "广东省", "广西壮族自治区", "海南省", "重庆市", "四川省", "贵州省",
  "云南省", "西藏自治区", "陕西省", "甘肃省", "青海省", "宁夏回族自治区", "新疆维吾尔自治区",
  "台湾省", "香港特别行政区", "澳门特别行政区"
)

# 设置随机种子以确保结果可重现
set.seed(123)

# 模拟降雨量数据（年降水量，单位：毫米）
# 考虑中国不同地区的气候特点
simulate_rainfall <- function() {
  # 南方地区（湿润）：800-2000mm
  south_provinces <- c("广东省", "广西壮族自治区", "海南省", "福建省", "江西省",
                      "湖南省", "湖北省", "浙江省", "上海市", "江苏省")

  # 西南地区（多雨）：600-1500mm
  southwest_provinces <- c("四川省", "重庆市", "贵州省", "云南省")

  # 北方地区（半干旱）：200-800mm
  north_provinces <- c("北京市", "天津市", "河北省", "山西省", "内蒙古自治区",
                      "辽宁省", "吉林省", "黑龙江省", "山东省", "河南省", "安徽省")

  # 西北地区（干旱）：50-400mm
  northwest_provinces <- c("陕西省", "甘肃省", "青海省", "宁夏回族自治区", "新疆维吾尔自治区")

  # 高原地区（较少）：200-600mm
  plateau_provinces <- c("西藏自治区")

  # 港澳台：800-2500mm
  special_regions <- c("台湾省", "香港特别行政区", "澳门特别行政区")

  rainfall_data <- data.frame(
    province = provinces,
    rainfall = numeric(length(provinces))
  )

  # 为不同地区分配不同的降雨量范围
  for(i in 1:length(provinces)) {
    province <- provinces[i]
    if(province %in% south_provinces) {
      rainfall_data$rainfall[i] <- runif(1, 800, 2000)
    } else if(province %in% southwest_provinces) {
      rainfall_data$rainfall[i] <- runif(1, 600, 1500)
    } else if(province %in% north_provinces) {
      rainfall_data$rainfall[i] <- runif(1, 200, 800)
    } else if(province %in% northwest_provinces) {
      rainfall_data$rainfall[i] <- runif(1, 50, 400)
    } else if(province %in% plateau_provinces) {
      rainfall_data$rainfall[i] <- runif(1, 200, 600)
    } else if(province %in% special_regions) {
      rainfall_data$rainfall[i] <- runif(1, 800, 2500)
    } else {
      rainfall_data$rainfall[i] <- runif(1, 300, 1000) # 默认值
    }
  }

  return(rainfall_data)
}

# 生成模拟数据
rainfall_data <- simulate_rainfall()

# 添加降雨等级分类
rainfall_data <- rainfall_data %>%
  mutate(
    rainfall_level = case_when(
      rainfall < 200 ~ "极少",
      rainfall < 400 ~ "较少",
      rainfall < 800 ~ "适中",
      rainfall < 1200 ~ "丰富",
      rainfall < 1600 ~ "很丰富",
      TRUE ~ "极丰富"
    ),
    rainfall_level = factor(rainfall_level,
                           levels = c("极少", "较少", "适中", "丰富", "很丰富", "极丰富"))
  )

# 查看数据
print(head(rainfall_data, 10))

2. 创建简化的省份坐标数据

# 由于获取完整的中国地图数据可能较复杂，这里创建一个简化版本
# 包含主要省份的大致坐标位置（用于散点图展示）

province_coords <- data.frame(
  province = c("北京市", "天津市", "河北省", "山西省", "内蒙古自治区",
               "辽宁省", "吉林省", "黑龙江省", "上海市", "江苏省",
               "浙江省", "安徽省", "福建省", "江西省", "山东省",
               "河南省", "湖北省", "湖南省", "广东省", "广西壮族自治区",
               "海南省", "重庆市", "四川省", "贵州省", "云南省",
               "西藏自治区", "陕西省", "甘肃省", "青海省", "宁夏回族自治区",
               "新疆维吾尔自治区"),
  longitude = c(116.4, 117.2, 114.5, 112.5, 111.7,
                123.4, 125.3, 126.6, 121.5, 118.8,
                120.2, 117.3, 119.3, 115.9, 117.0,
                113.7, 114.3, 113.0, 113.3, 108.3,
                110.3, 106.5, 104.1, 106.7, 102.7,
                91.1, 108.9, 103.8, 101.8, 106.3,
                87.6),
  latitude = c(39.9, 39.1, 38.0, 37.9, 40.8,
               41.8, 43.9, 45.8, 31.2, 32.0,
               30.3, 31.9, 26.1, 28.7, 36.7,
               34.8, 30.6, 28.2, 23.1, 22.8,
               20.0, 29.6, 30.7, 26.6, 25.0,
               29.7, 34.3, 36.1, 36.6, 38.5,
               43.8)
)

# 合并降雨量数据和坐标数据
rainfall_map_data <- merge(rainfall_data, province_coords, by = "province", all.x = TRUE)

数据可视化

1. 基础散点图可视化

# 创建基础的散点图来展示各省份降雨量
p1 <- ggplot(rainfall_map_data, aes(x = longitude, y = latitude)) +
  geom_point(aes(size = rainfall, color = rainfall), alpha = 0.7) +
  scale_color_viridis_c(name = "年降雨量\n(mm)", option = "plasma") +
  scale_size_continuous(name = "年降雨量\n(mm)", range = c(2, 15)) +
  labs(
    title = "中国各省份年降雨量分布",
    subtitle = "基于模拟数据",
    x = "经度",
    y = "纬度"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5, size = 12),
    legend.position = "right"
  )

print(p1)

2. 分类颜色可视化

# 使用降雨等级进行分类可视化
p2 <- ggplot(rainfall_map_data, aes(x = longitude, y = latitude)) +
  geom_point(aes(color = rainfall_level, size = rainfall), alpha = 0.8) +
  scale_color_brewer(name = "降雨等级", type = "seq", palette = "Blues") +
  scale_size_continuous(name = "年降雨量\n(mm)", range = c(3, 12)) +
  labs(
    title = "中国各省份降雨等级分布",
    subtitle = "按降雨量分级着色",
    x = "经度",
    y = "纬度"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5, size = 12),
    legend.position = "right"
  )

print(p2)

3. 添加省份标签

# 添加省份名称标签的可视化
p3 <- ggplot(rainfall_map_data, aes(x = longitude, y = latitude)) +
  geom_point(aes(color = rainfall, size = rainfall), alpha = 0.7) +
  geom_text(aes(label = substr(province, 1, 2)),
            vjust = -1, hjust = 0.5, size = 3, check_overlap = TRUE) +
  scale_color_viridis_c(name = "年降雨量\n(mm)", option = "viridis") +
  scale_size_continuous(name = "年降雨量\n(mm)", range = c(3, 10)) +
  labs(
    title = "中国各省份降雨量分布（带标签）",
    subtitle = "圆圈大小和颜色表示降雨量",
    x = "经度",
    y = "纬度"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5, size = 12),
    legend.position = "bottom"
  )

print(p3)

数据统计分析

降雨量统计摘要

# 基本统计信息
cat("中国各省份降雨量统计摘要:\n")
summary(rainfall_data$rainfall)

cat("\n按降雨等级分组统计:\n")
table(rainfall_data$rainfall_level)

# 创建统计图表
p4 <- ggplot(rainfall_data, aes(x = rainfall_level, fill = rainfall_level)) +
  geom_bar(alpha = 0.8) +
  scale_fill_brewer(type = "seq", palette = "Blues") +
  labs(
    title = "各降雨等级省份数量分布",
    x = "降雨等级",
    y = "省份数量"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 14, face = "bold"),
    legend.position = "none",
    axis.text.x = element_text(angle = 45, hjust = 1)
  )

print(p4)

降雨量分布直方图

# 降雨量分布直方图
p5 <- ggplot(rainfall_data, aes(x = rainfall)) +
  geom_histogram(bins = 15, fill = "skyblue", color = "white", alpha = 0.7) +
  geom_vline(aes(xintercept = mean(rainfall)),
             color = "red", linetype = "dashed", size = 1) +
  labs(
    title = "中国各省份年降雨量分布直方图",
    subtitle = paste("平均降雨量:", round(mean(rainfall_data$rainfall), 1), "mm"),
    x = "年降雨量 (mm)",
    y = "省份数量"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 14, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5, size = 12, color = "red")
  )

print(p5)

数据导出

# 保存数据到CSV文件
write.csv(rainfall_data, "中国省份降雨量模拟数据.csv", row.names = FALSE, fileEncoding = "UTF-8")

# 保存带坐标的数据
write.csv(rainfall_map_data, "中国省份降雨量地图数据.csv", row.names = FALSE, fileEncoding = "UTF-8")

cat("数据已保存到CSV文件\n")