Population
1. 배경
- 목표
- 1. 인구 소멸 위기 지역 파악
- 2. 인구 소멸 위기 지역의 지도 표현
- 3. 지도 표현에 대한 카르토그램 표현
데이터 읽고 인구 소멸 지역 계산하기
fillna()
datas = {
"A":np.random.randint(1, 45, 8),
"B":np.random.randint(1, 45, 8),
"C":np.random.randint(1, 45, 8),
}
fillna_df = pd.DataFrame(datas)
fillna_df
fillna_df.loc[2:4, ["A"]] = np.nan
fillna_df.loc[3:5, ["B"]] = np.nan
fillna_df.loc[4:7, ["C"]] = np.nan
fillna_df
fillna_df.fillna(value=0)
fillna_df.fillna(method="pad")
fillna_df.fillna(method="backfill")
fillna_df.fillna(method="ffill")
fillna_df.fillna(method="ffill", axis=0)
fillna_df.fillna(method="pad")
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
)
소계 제거
population = population[population["시도"] != "소계"]
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"
소멸지역을 조사하기 위한 데이터
population["20-39세"] = (
population["20 - 24세"] + population["25 - 29세"] + population["30 - 34세"] + population["35 - 39세"]
)
population["65세이상"] = (
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", "65세이상"]
)
소멸 비율 계산
pop["소멸비율"] = pop["20-39세", "여자"] / (pop["65세이상", "합계"] / 2)
소멸위기지역 컬럼 생성
pop["소멸위기지역"] = pop["소멸비율"] < 1.0
소멸위기지역 조회
pop[pop["소멸위기지역"] == True].index.get_level_values(1)
지도 시각화를 위한 지역별 ID 만들기
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", "기흥구", "수지구"],
"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]
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]
고성군
for idx, row in pop.iterrows():
if row["광역시도"][-3:] not in ["광역시", "특별시", "자치시"]:
if row["시도"][:-1] == "고성" and row["광역시도"] == "강원도":
si_name[idx] = "고성(강원)"
if row["시도"][:-1] == "고성" and row["광역시도"] == "경상남도":
si_name[idx] = "고성(경남)"
pop["ID"] = si_name
지도 그리기(카르토그램)
draw_korea_raw = pd.read_excel("../data/07_draw_korea_raw.xlsx")
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_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)]
]
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,
ha="center", # 수평 정렬
va="center", # 수직 정렬
linespacing=linespacing
)
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.fight_layout()
plt.show()
검증작업
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()))
merge
pop = pd.merge(pop, draw_korea, how="left", on=["ID"])
- 그림을 그리기 위한 데이터를 계산하는 함수
- 색상을 만들 때, 최소값을 흰색
- blockeMap : 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,
ha="center", # 수평 정렬
va="center", # 수직 정렬
linespacing=linespacing
)
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=(9, 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=2)
plt.gca().invert_yaxis()
plt.axis("off")
cb = plt.colorbar(shrink=0.1, aspect=10)
cb.set_label(targetData)
plt.tight_layout()
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"],
columns=[pop_folium.index, pop_folium["인구수합계"]],
fill_color="YlGnBu", #PuRd, YlGnBu
key_on="feature.id"
)
mymap
# 소멸위기지역 지도 시각화
mymap = folium.Map(location=[36.2002, 127.054], zoom_start=7)
mymap.choropleth(
geo_data=geo_str,
data=pop_folium["소멸위기지역"],
columns=[pop_folium.index, pop_folium["소멸위기지역"]],
fill_color="PuRd", #PuRd, YlGnBu
key_on="feature.id"
)
mymap