I've got a layer map plot in Altair of London that I was struggling to flip - Solving upside plot and projection problems in Geopandas and Altair
However, a more troubling problem is that I've got a dataframe with shapefiles that look a bit like shape_data and the points are the points.
Postcode Ward lat long geometry
0 N16 5RF Springfield 51.572046 -0.075572 POINT (-0.07557 51.57205)
1 N16 5RF Springfield 51.572046 -0.075572 POINT (-0.07557 51.57205)
2 N16 5RF Stamford Hill West 51.572046 -0.075572 POINT (-0.07557 51.57205)
3 N16 5RF Stamford Hill West 51.572046 -0.075572 POINT (-0.07557 51.57205)
The shape_data looks a bit like this:
NAME GSS_CODE DISTRICT LAGSSCODE HECTARES NONLD_AREA geometry
555 Hoxton East & Shoreditch E05009377 Hackney E09000012 102.355 0.0 POLYGON ((532942.69738 -182547.89554, 532938.50065 -182560.80185, 532968.49618 -182570.49907, 533030.40010 -182589.10375, 533033.09623 -182616.59588, 533034.39895 -182629.40221, 533033.89601 -182758.79517, 533032.79941 -182788.39670, 533031.99964 -182826.59577, 533031.29881 -182869.10360, 533033.69812 -182877.70114, 533035.99850 -182888.59802, 533039.40371 -182920.99874, 533042.70173 -182947.60113, 533055.00336 -182961.49715, 533061.50047 -182969.10497, 533063.99872 -182973.60369, 533068.69841 -182994.69765, 533071.89749 -183013.50227, 533073.99998 -183036.29574, 533074.40399 -183054.20062, 533072.90339 -183085.20174, 533073.80210 -183093.49937, 533070.80090 -183130.69872, 533066.39804 -183178.80495, 533061.89623 -183225.90147, 533060.50282 -183259.90173, 533060.19775 -183329.40184, 533026.50019 -183325.80287, 532990.79907 -183322.10393, 532960.69635 -183321.50410, 532923.89864 -183320.50439, 532838.90031 -183317.69519, 532835.99805 -183357.40383, 532833.59874 -183382.29670, 532829.59988 -183384.19616, 532824.19937 -183382.29670, 532809.80349 -183375.29870, 532736.39771 -183338.09935, 532718.10192 -183373.39925, 532688.60109 -183433.50204, 532625.60058 -183559.99583, 532590.30347 -183629.69588, 532627.70307 -183662.39652, 532660.09791 -183691.59816, 532691.50335 -183718.20055, 532725.29986 -183746.50245, 532745.20344 -183759.49873, 532767.39914 -183769.29592, 532786.00000 -183775.10426, 532799.80223 -183774.00457, 532801.50071 -183770.39561, 532815.69871 -183774.50443, 532869.70386 -183787.80063, 532922.90099 -183798.69751, 532939.10254 -183801.89659, 532951.99781 -183804.19593, 532964.00262 -183804.49585, 532992.59649 -183799.49728, 533054.09640 -183786.70094, 533142.49995 -183769.39589, 533255.49851 -183747.40219, 533293.40106 -183740.90405, 533369.70085 -183724.99860, 533386.70217 -183721.99946, 533449.39761 -183711.00261, 533476.69701 -183705.40421, 533475.40254 -183547.39944, 533473.20111 -183489.89590, 533470.49673 -183412.99791, 533467.60271 -183358.40354, 533467.29764 -183337.19961, 533466.70400 -183324.70319, 533465.59916 -183312.99654, 533471.89839 -183259.10196, 533475.30360 -183235.09883, 533478.79950 -183205.89719, 533479.69821 -183182.70383, 533479.20351 -183162.79953, 533478.40374 -183142.09546, 533477.00208 -183129.79898, 533474.70171 -183093.19945, 533472.79710 -183073.79501, 533471.99733 -183066.29715, 533471.09862 -183056.20004, 533470.80179 -183051.40142, 533463.19985 -182999.59625, 533460.00076 -182978.50228, 533452.29988 -182940.20325, 533447.09724 -182915.50032, 533440.30331 -182892.29696, 533436.09833 -182878.80082, 533429.60122 -182847.19987, 533432.89925 -182846.50007, 533443.99710 -182843.30099, 533460.39653 -182839.40210, 533470.49673 -182836.70287, 533487.30016 -182832.80399, 533501.69604 -182828.99508, 533524.30399 -182823.29671, 533540.59624 -182819.89768, 533558.70239 -182815.69889, 533567.40093 -182813.39954, 533575.10182 -182807.90112, 533580.60127 -182803.80229, 533560.10405 -182777.69976, 533539.30176 -182749.59781, 533502.99876 -182707.19994, 533478.29655 -182683.59670, 533543.80356 -182668.60099, 533541.70107 -182637.99975, 533530.10027 -182597.50134, 533523.80105 -182583.69530, 533523.80105 -182570.99893, 533523.99893 -182557.90268, 533525.40059 -182539.69789, 533528.09672 -182510.69619, 533532.10382 -182466.29890, 533543.30062 -182350.40208, 533579.80150 -182355.20070, 533590.80041 -182303.69545, 533595.59904 -182273.90397, 533595.30222 -182262.49724, 533593.19973 -182228.29703, 533590.50359 -182212.80146, 533589.10193 -182204.90372, 533587.60133 -182196.69607, 533578.10302 -182158.19709, 533574.09592 -182138.80265, 533568.80259 -182118.89834, 533567.30199 -182113.39992, 533549.20408 -182097.40450, 533530.29815 -182119.09829, 533501.90216 -182107.50161, 533447.50125 -182086.89750, 533425.99813 -182076.50048, 533411.89908 -182041.60047, 533410.70354 -182037.90153, 533320.19751 -182053.39709, 533250.89777 -182075.90065, 533230.49949 -182082.49876, 533215.79854 -182024.09548, 533198.59935 -181977.99868, 533184.59923 -181948.19721, 533182.39780 -181945.39801, 533167.49898 -181925.30376, 533146.80387 -181897.90160, 533119.19941 -181869.79965, 533078.89755 -181840.49803, 532946.10259 -181894.90246, 532955.69985 -181928.00299, 532965.80005 -181968.70134, 532978.10168 -182037.30170, 532989.19953 -182089.29682, 532993.70133 -182136.00345, 532998.20314 -182237.50439, 532999.20079 -182259.79801, 533004.70024 -182293.29842, 532983.60113 -182292.39868, 532941.09784 -182294.29814, 532941.60079 -182350.20213, 532918.30025 -182358.99961, 532920.40274 -182368.09701, 532929.29916 -182396.79880, 532944.89882 -182436.89732, 532963.69755 -182460.80047, 532951.39592 -182511.79588, 532942.69738 -182547.89554))
I can write the code in Python's Altair to plot the data
london_wards_map = alt.Chart(london_wards_gpd).mark_geoshape(
fill=None, # No fill
stroke='darkgray', # Black stroke
strokeWidth=1 # Stroke width ).encode(
tooltip='NAME:N' # Replace 'NAME' with the actual name of the column that contains the ward names ).properties(
width=800,
height=600 ).project(
type='identity')
postcode_meals.crs = 4326
points = alt.Chart(postcode_meals).mark_circle(color='#008751').encode(
longitude='long:Q',
latitude='lat:Q',
size=alt.Size('count:Q', scale=alt.Scale(domain=[postcode_meals['count'].min(), postcode_meals['count'].max()], range=[10, 1000])), # Adjust the range as needed
tooltip=['Ward', 'Postcode', 'count', 'long', 'lat']
).properties(title='Map of vouchers by postcode')
text = alt.Chart(postcode_meals).mark_text(dy=-5).encode(
longitude='long:Q',
latitude='lat:Q',
text='Postcode'
)
shape_data = {'NAME': {555: 'Hoxton East & Shoreditch',
556: 'Haggerston',
557: 'De Beauvoir',
558: 'London Fields',
559: 'Hackney Wick'},
'GSS_CODE': {555: 'E05009377',
556: 'E05009375',
557: 'E05009371',
558: 'E05009381',
559: 'E05009374'},
'DISTRICT': {555: 'Hackney',
556: 'Hackney',
557: 'Hackney',
558: 'Hackney',
559: 'Hackney'},
'LAGSSCODE': {555: 'E09000012',
556: 'E09000012',
557: 'E09000012',
558: 'E09000012',
559: 'E09000012'},
'HECTARES': {555: 102.355,
556: 86.724,
557: 59.347,
558: 101.739,
559: 163.387},
'NONLD_AREA': {555: 0.0, 556: 0.0, 557: 0.0, 558: 0.0, 559: 0.0},
'geometry': {555: <POLYGON ((532942.697 -182547.896, 532938.501 -182560.802, 532968.496 -18257...>,
556: <POLYGON ((534479.503 -183624.697, 534466.698 -183600.304, 534458.098 -18357...>,
557: <POLYGON ((533479.5 -183988.203, 533451.096 -183988.003, 533450.7 -183978.79...>,
558: <POLYGON ((533479.5 -183988.203, 533479.5 -184005.498, 533479.698 -184015.39...>,
559: <POLYGON ((537572.901 -185494.702, 537509.497 -185492.903, 537446.1 -185494....>}}
points = {'Postcode': {0: 'N16 5RF', 1: 'N16 5RF', 2: 'N16 5RF', 3: 'N16 5RF'},
'Ward': {0: 'Springfield',
1: 'Springfield',
2: 'Stamford Hill West',
3: 'Stamford Hill West'},
'lat': {0: 51.572046, 1: 51.572046, 2: 51.572046, 3: 51.572046},
'long': {0: -0.075572, 1: -0.075572, 2: -0.075572, 3: -0.075572},
'geometry': {0: <POINT (-0.076 51.572)>,
1: <POINT (-0.076 51.572)>,
2: <POINT (-0.076 51.572)>,
3: <POINT (-0.076 51.572)>}}
But when I tried that I get this.
The CRS setting is right but for some reason the inner coords of the boundaries are an several orders of magnitude beyond typical latitude and longitudes.
The problem I had was that the data was loaded into a dataframe rather than directly from a file using geopandas.GeoDataframe().
My dataframe was called london_wards_shp and just needed the geometry to be loaded using shapely's wkt method, and knowing it was eastings and northings just needed to be converted to lat longs.