Geoviews：覆盖有英国Choropleth的地图图块无法精确对齐

Question

我有一个基于国家统计局邮政编码查找文件的~107K行csv，该文件已作为DataFrame df加载。

（（请注意，此链接用于原始ONS文件，而不是我更改的文件）

https://ons.maps.arcgis.com/home/item.html?id=4f71f3e9806d4ff895996f832eb7aacf

import pandas as pd
import numpy as np
import random
import copy
import plotly.express as px
import panel as pn
import holoviews as hv
import geoviews as gv
import geoviews.feature as gf
import cartopy
import cartopy.feature as cf
from geoviews import opts
from cartopy import crs as ccrs

pn.extension("plotly")
gv.extension("bokeh")

df = pd.read_csv("../df_example_dataset_221119.csv") 

df的第1 25行：

              nspl_easting  nspl_latitude nspl_localAuthorityDistrict  nspl_longitude  nspl_northing  ... postcode_prefix postcode_order                   supergroup                             group                                     subgroup
    0         205580      52.016209                   W06000009       -4.834655         239104  ...              SA            111                    Urbanites               Ageing urban living                   Self-sufficient retirement
    1         449021      51.740288                   E07000180       -1.291435         204857  ...              OX             91                    Urbanites               Ageing urban living                          Communal retirement
    2         443448      53.508308                   E08000018       -1.346353         401490  ...               S            119          Hard-pressed living           Industrious communities                      Industrious transitions
    3         415327      54.619576                   E06000047       -1.764161         524962  ...              DL             60          Hard-pressed living           Industrious communities                         Industrious hardship
    4         461888      52.962238                   E07000173       -1.080085         340937  ...              NG            115                    Urbanites  Urban professionals and families               Families in terraces and flats
    5         441508      50.915695                   E06000045       -1.410917         113080  ...              SO             89                Cosmopolitans            Students around campus                      Student communal living
    6         393588      52.553703                   E08000031       -2.096000         295100  ...              WV             43                 Suburbanites            Semi-detached suburbia                        Multi-ethnic suburbia
    7         440238      53.196899                   E07000038       -1.399148         366815  ...               S            119          Hard-pressed living           Industrious communities                         Industrious hardship
    8         197093      56.462948                   S12000035       -5.295074         734958  ...              PA             33              Rural residents                     Rural tenants                    Ageing rural flat tenants
    9         652748      52.603550                   E07000145        1.731206         307179  ...              NR            108  Multicultural metropolitans              Rented family living                 Private renting new arrivals
    10        443254      53.615883                   E08000036       -1.347626         413457  ...              WF             70                    Urbanites               Ageing urban living                   Self-sufficient retirement
    11        244584      52.002270                   W06000010       -4.265472         236183  ...              SA            111              Rural residents             Ageing rural dwellers                     Renting rural retirement
    12        342359      53.394840                   E08000012       -2.868276         389019  ...               L             93    Constrained city dwellers                 White communities                   Constrained young families
    13        317949      56.591812                   S12000048       -3.337703         745234  ...              PH             11    Constrained city dwellers              Challenged diversity  Transitional Eastern European neighbourhood
    14        522743      51.418400                   E09000024       -0.236223         170296  ...              SW             95                 Suburbanites                Suburban achievers                        Indian tech achievers
    15        416903      50.993282                   E06000054       -1.760528         121570  ...              SP             32              Rural residents                     Rural tenants                    Ageing rural flat tenants
    16        412438      53.683681                   E08000033       -1.813157         420819  ...              HX             13                    Urbanites               Ageing urban living                   Self-sufficient retirement
    17        380633      53.561138                   E08000002       -2.293852         407209  ...              BL             45                    Urbanites               Ageing urban living                           Delayed retirement
    18        465475      50.795639                   E06000044       -1.072351          99975  ...              PO            112  Multicultural metropolitans              Rented family living                 Private renting new arrivals
    19        525069      51.103195                   E07000226       -0.215022         135281  ...              RH             74  Multicultural metropolitans              Rented family living                Social renting young families
    20        324536      55.648670                   S12000026       -3.200686         640113  ...              EH            110                 Suburbanites                Suburban achievers                           Ageing in suburbia
    21        317378      54.844311                   E07000026       -3.288153         550704  ...              CA             58              Rural residents                     Rural tenants                                   Rural life
    22        491761      51.034000                   E07000225       -0.692730         126884  ...              GU            104              Rural residents               Farming communities                     Agricultural communities
    23        449460      54.650303                   E06000001       -1.234992         528624  ...              TS             81                 Suburbanites            Semi-detached suburbia                         Semi-detached ageing
    24        457016      52.624683                   E06000016       -1.159144         303321  ...              LE            103  Multicultural metropolitans              Rented family living                 Private renting new arrivals

数据类型：

    In [35]: df.info()                                                                                                                                                                                                                          
    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 107632 entries, 0 to 107631
    Data columns (total 14 columns):
    nspl_easting                                 107632 non-null int32
    nspl_latitude                                107632 non-null float32
    nspl_localAuthorityDistrict                  107632 non-null category
    nspl_longitude                               107632 non-null float32
    nspl_northing                                107632 non-null int32
    nspl_outputAreaClassification                107632 non-null category
    nspl_ward                                    107632 non-null category
    nspl_westminsterParliamentaryConstituency    107632 non-null category
    postcode                                     107632 non-null object
    postcode_prefix                              107632 non-null category
    postcode_order                               107632 non-null int32
    supergroup                                   107632 non-null category
    group                                        107632 non-null category
    subgroup                                     107632 non-null category
    dtypes: category(8), float32(2), int32(3), object(1)
    memory usage: 4.4+ MB

postcode_prefix列包含所有121个英国邮政编码区域。

In [50]: df["postcode_prefix"].value_counts()                                                                                                                                                                                               
Out[50]: 
BT    2978
B     2625
S     2099
M     1988
NE    1975
      ... 
WC     165
LD     140
KW     108
HS      53
ZE      30
Name: postcode_prefix, Length: 121, dtype: int64

postcode_order列包含数字1-121。1 = postcode_prefix（df）中ie ZE的最低计数，121 = ie BT中df的最高计数[]。

In [42]: df["postcode_order"].value_counts()                                                                                                                                                                                                
Out[42]: 
121    2978
120    2625
119    2099
118    1988
117    1975
       ... 
5       165
4       140
3       108
2        53
1        30
Name: postcode_order, Length: 121, dtype: int64

英国Shapefile从以下位置下载：

https://geoportal.statistics.gov.uk/datasets/bbb0e58b0be64cc1a1460aa69e33678f_0/data

shapefile = "../Local_Authority_Districts_April_2019_Boundaries_UK_BUC/Local_Authority_Districts_April_2019_Boundaries_UK_BUC.shp"
gv.Shape.from_shapefile(shapefile, crs=ccrs.Mercator())

In [51]: shapes = cartopy.io.shapereader.Reader(shapefile) 
    ...: list(shapes.records())[0]                                                                                                                                                                                                          
Out[51]: <Record: <shapely.geometry.polygon.Polygon object at 0x1c35f30a50>, {'objectid': 1, 'lad19cd': 'E06000001', 'lad19nm': 'Hartlepool', 'lad19nmw': None, 'bng_e': 447157, 'bng_n': 531476, 'long': -1.27023005, 'lat': 54.67620087, 'st_areasha': 96512310.88728464, 'st_lengths': 50488.38708066442}, <fields>>

设置纬度和经度并绘制曲线图

heatmap = gv.Shape.from_records(shapes.records(), df, on={"lad19cd":"nspl_localAuthorityDistrict"}, value="postcode_order",
                      index=["postcode_prefix","nspl_outputAreaClassification","supergroup","group","subgroup"], crs=ccrs.Orthographic(central_longitude=-8.00, central_latitude=50.00)).opts(tools=["hover"], cmap = "Reds", colorbar=True, title="UK Postcode Heatmap", width=800, height=1350, alpha=0.4)
heatmap

设置地图图块

wikimap =hv.Tiles('https://maps.wikimedia.org/osm-intl/{Z}/{X}/{Y}@2x.png', name="Wikipedia").opts(width=800, height=1350)

最终图解，包括设置点标记（此处使用的经度和纬度是针对大本钟的]

wikimap * heatmap * gv.Points([(-0.116773,51.510357)]).opts(color='purple', size = 20) 

结果图：

正如您在下面看到的那样，我的波形图几乎成功地几乎覆盖在地图图块上。但是，无论我如何调整central_longitude和central_latitude值，我都无法使它们精确对齐。实际上，似乎我需要稍微旋转我的弦轴。

我怀疑我的问题是由以下原因引起的：

1。我正在使用的shapefile

或

2。我选择的投影

[我试图使两个投影都等于crs=ccrs.Mercator()，但是这不允许我为我的弦速设置latitude和longitude，因此无法将其成功覆盖在地图图块上。

朝正确方向的任何指针将不胜感激。

谢谢

Answer 1

[19/11/23更新：

发布此问题后，我发现：https://scitools.org.uk/cartopy/docs/v0.15/crs/projections.html

[我在这里找到投影= OSGB。切换到此投影会产生我想要的结果：