20_EDA 5

김정연·2023년 6월 30일
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데이터스쿨

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Population

1. 배경

  • 목표
      1. 인구 소멸 위기 지역 파악
      1. 인구 소멸 위기 지역의 지도 표현
      1. 지도 표현애 대한 카르토그램 표현

2. 데이터 읽고 인구 소멸 지역 계산하기

import platform
import matplotlib.pyplot as plt
from matplotlib import font_manager, rc

path = "c:/Windows/Fonts/malgun.ttf"

if platform.system() == "Darwin":
    print("Hangul OK in your MAC !!!")
    rc("font", family="AppleGothic")
elif platform.system() == "Windows":
    font_name = font_manager.FontProperties(fname=path).get_name()
    print("Hangul OK in your Windows !!!")
    rc("font", family=font_name)
else:
    print("Unknown system... sorry~~~~")

plt.rcParams["axes.unicode_minus"] = False
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import warnings

warnings.filterwarnings(action="ignore")
%matplotlib inline
population = pd.read_excel("../data/07_population_raw_data.xlsx", header=1)
population.fillna(method="pad", inplace=True)
population

# 컬럼 이름 변경

population.rename(
    columns={
        "행정구역(동읍면)별(1)" : " 광역시도",
        "행정구역(동읍면)별(2)" : "시도",
        "계" : "인구수"
        
    }, inplace=True)

population.head()

# 소계 제거

population = population[population["시도"] != "소계"]
population.head()
population.is_copy = False

population.rename(
    columns={"항목": "구분"}, inplace=True)
population.loc[population["구분"] == "총인구수 (명)", "구분"] = "합계"
population.loc[population["구분"] == "남자인구수 (명)", "구분"] = "남자"
population.loc[population["구분"] == "여자인구수 (명)", "구분"] = "여자"

population

## 소멸지역을 조사하기 위한 데이터

population["20-39세"] = (
    population["20 - 24세"] + population["25 - 29세"] + population["30 - 34세"] + population["35 - 39세"]
)

population["65세 이상"] = (
    population["65 - 69세"] + 
    population["70 - 74세"] +
    population["75 - 79세"] +
    population["80 - 84세"] +
    population["85 - 89세"] +
    population["90 - 94세"] +
    population["95 - 99세"] +
    population["100+"] 
)
# pivot_table
pop = pd.pivot_table(
    data = population,
    index=["광역시도","시도"],
    columns=["구분"],
    values=["인구수", "20-39세", "65세 이상"]
)

pop

# 소멸 비율 계산

pop["소멸비율"] = pop["20-39세", "여자"] /(pop["65세 이상", "합계"] / 2)
pop.tail()
# 소멸 위기 지역 컬럼 생성

pop["소멸위기지역"] = pop["소멸비율"] < 1.0
pop

# 소멸위기지역 조회
pop[pop["소멸위기지역"]==True].index.get_level_values(1)

pop.reset_index(inplace=True)
len(pop.columns.get_level_values(0))

#출력 : 13
tmp_columns=[
    pop.columns.get_level_values(0)[n] + pop.columns.get_level_values(1)[n]
    for n in range(0, len(pop.columns.get_level_values(0)))
]

pop_columns = tmp_columns

pop.head()

3. 지도 시각화를 위한 지역별 ID 만들기

pop["시도"].unique()

si_name = [None] * len(pop)
tmp_gu_dict = {
    "수원" : ["장안구", "권선구", "팔달구", "영통구"],
    "성남" : ["수정구", "중원구", "분당구"],
    "안양" : ["만안구", "동안구"],
    "안산" : ["상록구", "단원구"],
    "고양" : ["덕양구", "일산동구", "일산서구"],
    "용인" : ["처인구", "수지구", "기흥구"],
    "청주" : ["상당구", "서원구", "흥덕구", "청원구"],
    "천안" : ["동남구", '서북구'],
    "전주" : ["완산구", "덕진구"],
    "포항" : ["남구", "북구"],
    "창원" : ["의창구", "성산구", "진해구", "마산합포구", "마산회원구"],
    "부천" : ["오정구", "원미구", "소사구"],
}

(1). 일반 시 이름과 세종시, 광역시도 일반 구 정리

for idx , row in pop.iterrows():
    if row["광역시도"][-3:] not in ["광역시", "특별시", "자치시"]:
        si_name[idx] = row['시도'][:-1]
        
    elif row["광역시도"] == "세종특별자치시":
            si_name[idx] = "세종"
    else:
        if len(row["시도"]) == 2:
            si_name[idx] = row["광역시도"][:2] + " " + row["시도"]
                
        else:
            si_name[idx] = row["광역시도"][:2] + " " + row["시도"][:-1]

(2) 행정구

for idx, row in pop.iterrows():
    if row["광역시도"][-3:] not in ["광역시", "특별시", "자치시"]:
        for keys, values in tmp_gu_dict.items():
            if row["시도"] in values:
                if len(row["시도"]) == 2:
                    si_name[idx] = keys + " " + row["시도"]
                    
                elif row["시도"] in ["마산합포구", "마산회원구"]:
                    si_name[idx] = keys + " " + row["시도"][2: -1]
                else:
                    si_name[idx] = keys + " " + row["시도"][: -1]

(3) 고성군

for idx, row in pop.iterrows():
    if row["광역시도"][-3:] not in ["광역시도", "특별시", "자치시"]:
        if row["시도"][:-1] == "고성" and  row["광역시도"] =="강원도":
            si_name[idx] = "고성(강원)"
        elif row["시도"][:-1] == "고성" and  row["광역시도"] =="경상남도":
            si_name[idx] = "고성(경남)"
pop["ID"] = si_name
pop

del pop["20-39세남자"]
del pop["65세 이상남자"]
del pop["65세 이상여자"]

pop.head()

4. 지도 그리기(카르토그램)

draw_korea_raw = pd.read_excel("../data/07_draw_korea_raw.xlsx")

draw_korea_raw.stack()

draw_korea_raw_stacked = pd.DataFrame(draw_korea_raw.stack())
draw_korea_raw_stacked.reset_index(inplace=True)
draw_korea_raw_stacked.rename(
    columns={
        "level_0": "y",
        "level_1": "x",
        0: "ID"
    }, inplace=True
)

draw_korea = draw_korea_raw_stacked
BORDER_LINES = [
    [(5, 1), (5, 2), (7, 2), (7, 3), (11, 3), (11, 0)], # 인천
    [(5, 4), (5, 5), (2, 5), (2, 7), (4, 7), (4, 9), (7, 9), (7, 7), (9, 7), (9, 5), (10, 5), (10, 4), (5, 4)], # 서울
    [(1, 7), (1, 8), (3, 8), (3, 10), (10, 10), (10, 7), (12, 7), (12, 6), (11, 6), (11, 5), (12, 5), (12, 4), (11, 4), (11, 3)], # 경기도
    [(8, 10), (8, 11), (6, 11), (6, 12)], # 강원도
    [(12, 5), (13, 5), (13, 4), (14, 4), (14, 5), (15, 5), (15, 4), (16, 4), (16, 2)], # 충청북도
    [(16, 4), (17, 4), (17, 5), (16, 5), (16, 6), (19, 6), (19, 5), (20, 5), (20, 4), (21, 4), (21, 3), (19, 3), (19, 1)], # 전라북도
    [(13, 5), (13, 6), (16, 6)], 
    [(13, 5), (14, 5)], # 대전시 # 세종시
    [(21, 2), (21, 3), (22, 3), (22, 4), (24, 4), (24, 2), (21, 2)], # 광주
    [(20, 5), (21, 5), (21, 6), (23, 6)], # 전라남도
    [(10, 8), (12, 8), (12, 9), (14, 9), (14, 8), (16, 8), (16, 6)], # 충청북도
    [(14, 9), (14, 11), (14, 12), (13, 12), (13, 13)], # 경상북도
    [(15, 8), (17, 8), (17, 10), (16, 10), (16, 11), (14, 11)], # 대구
    [(17, 9), (18, 9), (18, 8), (19, 8), (19, 9), (20, 9), (20, 10), (21, 10)], # 부산
    [(16, 11), (16, 13)],
    [(27, 5), (27, 6), (25, 6)]
]
draw_korea["ID"][13].split()[1]

#출력 : '일산동
def plot_text_simple(draw_korea):
    for idx, row in draw_korea.iterrows():
        if len(row["ID"].split()) == 2:
            dispname = "{}\n{}".format(row["ID"].split()[0], row["ID"].split()[1])
        elif row["ID"][:2] == "고성":
            dispname = "고성"
        else:
            dispname = row["ID"]
            
        if len(dispname.splitlines()[-1]) >= 3:
            fontsize, linespacing = 9.5, 1.5
        else:
            fontsize, linespacing = 11, 1.2


        plt.annotate(
            dispname,
            (row["x"] + 0.5, row["y"] + 0.5),
            weight="bold",
            fontsize=fontsize,
            linespacing=linespacing,
            ha="center", # 수평 정렬
            va="center", # 수직 정렬 
        )
def simpleDraw(draw_korea):
    plt.figure(figsize=(8, 11))
    
    plot_text_simple(draw_korea)
    
    for path in BORDER_LINES:
        ys, xs = zip(*path)
        plt.plot(xs, ys, c="black", lw=1.5)
    
    plt.gca().invert_yaxis()
    plt.axis("off")
    plt.tight_layout()
    plt.show()
simpleDraw(draw_korea)

검증 작업

set(draw_korea["ID"].unique()) - set(pop["ID"].unique())

#출력 : set()
set(pop["ID"].unique()) - set(draw_korea["ID"].unique())

#출력 : {'고양', '부천', '성남', '수원', '안산', '안양', '용인', '전주', '창원', '천안', '청주', '포항'}
tmp_list = list(set(pop["ID"].unique()) - set(draw_korea["ID"].unique()))

for tmp in tmp_list:
    pop = pop.drop(pop[pop["ID"] == tmp].index)
print(set(pop["ID"].unique()) - set(draw_korea["ID"].unique()))

#출력 : set()
pop = pd.merge(pop, draw_korea, how="left", on="ID")
pop.head()

그림을 그리기 위한 데이터를 계산하는 함수

  • 색상을 만들 때, 최소값을 흰색
  • blockedMap: 인구현황(pop)
  • targetData: 그리고 싶은 컬럼
def get_data_info(targetData, blockedMap):
    whitelabelmin = (
        max(blockedMap[targetData]) - min(blockedMap[targetData])
    ) * 0.25 + min(blockedMap[targetData])
    vmin = min(blockedMap[targetData])
    vmax = max(blockedMap[targetData])
    
    mapdata = blockedMap.pivot_table(index="y", columns="x", values=targetData)

    return mapdata, vmax, vmin, whitelabelmin
def get_data_info_for_zero_center(targetData, blockedMap):
    whitelabelmin = 5 
    tmp_max = max(
        [np.abs(min(blockedMap[targetData])), np.abs(max(blockedMap[targetData]))]
    )
    vmin, vmax = -tmp_max, tmp_max
    mapdata = blockedMap.pivot_table(index="y", columns="x", values=targetData)
    return mapdata, vmax, vmin, whitelabelmin
def plot_text(targetData, blockedMap, whitelabelmin):
    for idx, row in blockedMap.iterrows():
        if len(row["ID"].split()) == 2:
            dispname = "{}\n{}".format(row["ID"].split()[0], row["ID"].split()[1])
        elif row["ID"][:2] == "고성":
            dispname = "고성"
        else:
            dispname = row["ID"]
            
        if len(dispname.splitlines()[-1]) >= 3:
            fontsize, linespacing = 9.5, 1.5
        else:
            fontsize, linespacing = 11, 1.2


        annocolor = "white" if np.abs(row[targetData]) > whitelabelmin else "black"
        plt.annotate(
            dispname,
            (row["x"] + 0.5, row["y"] + 0.5),
            weight="bold",
            color=annocolor,
            fontsize=fontsize,
            linespacing=linespacing,
            ha="center", # 수평 정렬
            va="center", # 수직 정렬 
        )
def drawKorea(targetData, blockedMap, cmapname, zeroCenter=False):
    if zeroCenter:
        masked_mapdata, vmax, vmin, whitelabelmin = get_data_info_for_zero_center(targetData, blockedMap)
    
    if not zeroCenter:
        masked_mapdata, vmax, vmin, whitelabelmin = get_data_info(targetData, blockedMap)
        
    plt.figure(figsize=(8, 11))
    plt.pcolor(masked_mapdata, vmin=vmin, vmax=vmax, cmap=cmapname, edgecolor="#aaaaaa", linewidth=0.5)
    
    plot_text(targetData, blockedMap, whitelabelmin)
    
    for path in BORDER_LINES:
        ys, xs = zip(*path)
        plt.plot(xs, ys, c="black", lw=1.5)
    
    plt.gca().invert_yaxis()
    plt.axis("off")
    plt.tight_layout()
    cb = plt.colorbar(shrink=0.1, aspect=10)
    cb.set_label(targetData)
    plt.show()
drawKorea("인구수합계", pop, "Blues")

pop["소멸위기지역"] = [1 if con else 0 for con in pop["소멸위기지역"]]
drawKorea("소멸위기지역", pop, "Reds")

pop["여성비"] = (pop["인구수여자"] / pop["인구수합계"] - 0.5) * 100
drawKorea("여성비", pop, "RdBu", zeroCenter=True)

pop["2030여성비"] = (pop["20-39세여자"] / pop["20-39세합계"] - 0.5) * 100
drawKorea("2030여성비", pop, "RdBu", zeroCenter=True)

import folium
import json 

pop_folium = pop.set_index("ID")
pop_folium.head()
geo_path = "../data/07_skorea_municipalities_geo_simple.json"
geo_str = json.load(open(geo_path, encoding="utf-8"))

# 인구수합계 지도 시각화

mymap = folium.Map(location=[36.2002, 127.054], zoom_start=7)
mymap.choropleth(
    geo_data=geo_str,
    data=pop_folium["인구수합계"],
    key_on="feature.id",
    columns=[pop_folium.index, pop_folium["인구수합계"]],
    fill_color="YlGnBu"
)

mymap

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