| import json, os, glob, textwrap, numpy as np, pandas as pd, matplotlib |
| matplotlib.use('Agg') |
| import matplotlib.pyplot as plt |
| from matplotlib import font_manager as fm |
| from matplotlib.patches import Polygon as MPoly |
| from matplotlib.lines import Line2D |
| from shapely.geometry import shape, box |
|
|
| for f in glob.glob('fonts/*.otf') + ['/usr/share/fonts/opentype/noto/NotoSansCJK-Regular.ttc', '/usr/share/fonts/opentype/noto/NotoSansCJK-Bold.ttc']: |
| if os.path.exists(f): fm.fontManager.addfont(f) |
| _n = {f.name for f in fm.fontManager.ttflist} |
| FONT = next((n for n in ('Noto Sans CJK SC', 'Noto Sans CJK JP') if n in _n), 'DejaVu Sans') |
| plt.rcParams.update({'font.family': FONT, 'font.size': 10, 'axes.edgecolor': '#BBBBBB', 'axes.linewidth': .6, |
| 'xtick.color': '#666666', 'ytick.color': '#666666', 'axes.labelcolor': '#444444', 'text.color': '#1F1F1F', |
| 'savefig.dpi': 200}) |
| INK, MID, LIGHT, LAND, PAPER = '#1F1F1F', '#8A8A8A', '#C9C6BF', '#ECE9E3', '#FFFFFF' |
| RED, RED_SOFT, BLUE, BLUE_SOFT = '#B33A2B', '#E3B5AE', '#2F4B7C', '#B9C4D6' |
| os.makedirs('out/assets', exist_ok=True) |
|
|
| pol = pd.read_parquet('out/data/policy_timeline.parquet') |
| ind = pd.read_parquet('out/data/indicators.parquet') |
| comp = pd.read_parquet('out/data/competition.parquet') |
| pl = pd.read_parquet('out/data/places.parquet') |
| KIND = {'Policy': (RED, 'o'), 'Law': (INK, 's'), 'Institution': (RED, 'D'), 'Accounting': (BLUE, 'D'), 'Exchange': (BLUE, 'o'), 'Competition': (MID, '^')} |
| v = lambda k, y: ind[(ind.indicator == k) & (ind.year == y)].value.iloc[0] |
|
|
| def title(fig, t, s, y=.95): |
| fig.text(.05, y, t, fontsize=15, weight='bold') |
| fig.text(.05, y - .045, s, fontsize=9, color='#555555') |
| def clean(ax, left=True): |
| for s_ in ('top', 'right'): ax.spines[s_].set_visible(False) |
| if not left: ax.spines['left'].set_visible(False) |
| ax.tick_params(length=0) |
|
|
| |
| fig, ax = plt.subplots(figsize=(13, 8)) |
| d = pol.sort_values('date').reset_index(drop=True) |
| x = d.date.dt.year + (d.date.dt.dayofyear - 1) / 366 |
| for i, r in d.iterrows(): |
| col, mk = KIND[r.kind] |
| ax.hlines(i, 2014.6, x[i], color='#F0F0F0', lw=.8, zorder=1) |
| ax.scatter(x[i], i, color=col, marker=mk, s=60, zorder=3) |
| ax.text(x[i] + .12, i, f"{r.date:%b %Y} {r.event_zh}", va='center', fontsize=7.6, color=MID) |
| ax.set_yticks(range(len(d))); ax.set_yticklabels(d.event, fontsize=8.2); ax.set_ylim(len(d) - .4, -1.2) |
| ax.axvspan(2024, 2027, color=LAND, lw=0, zorder=0); ax.text(2024.05, -.9, '"Data Element ×" plan, 2024 to 2026', fontsize=7.5, color=MID, va='bottom') |
| ax.set_xlim(2014.6, 2033.5); ax.set_xticks(range(2015, 2027)); clean(ax, left=False); ax.xaxis.grid(color='#F2F2F2', lw=.6); ax.set_axisbelow(True) |
| fig.legend(handles=[Line2D([], [], marker=m, ls='', color=c, label=k, ms=7) for k, (c, m) in KIND.items()], loc='upper left', bbox_to_anchor=(.045, .9), ncol=6, frameon=False, fontsize=8.5) |
| title(fig, 'From big data to data as a factor of production', 'The main laws, policies, institutions and markets for data in China, 2015 to 2026.', y=.965) |
| fig.text(.05, .02, 'Curated for this dataset from official releases (State Council, NPC, Ministry of Finance, National Data Administration, CAC) and press reports. ' |
| 'Date of adoption, publication or opening.', fontsize=7, color=MID) |
| plt.subplots_adjust(left=.36, right=.98, top=.85, bottom=.07) |
| fig.savefig('out/assets/policy_timeline.png', facecolor=PAPER); plt.close(fig) |
|
|
| |
| fig, axs = plt.subplots(1, 4, figsize=(14, 5.2), gridspec_kw=dict(width_ratios=[1.1, .8, 1, 1.1], wspace=.55)) |
| a = axs[0]; yrs = [2023, 2024, 2025]; vals = [v('data_production', y) for y in yrs] |
| a.bar(yrs, vals, color=[LIGHT, LIGHT, RED], width=.6) |
| for y, val in zip(yrs, vals): a.text(y, val + 1, f'{val:.1f}', ha='center', fontsize=9) |
| for y, g in [(2024, 25.0), (2025, 27.28)]: a.text(y, 3, f'+{g:.0f}%', ha='center', fontsize=8, color=PAPER, weight='bold') |
| a.set_xticks(yrs); a.set_ylim(0, 62); a.set_yticks([]); clean(a, left=False) |
| a.set_title('Data produced in China\n(zettabytes a year)', fontsize=9.5, loc='left') |
| a = axs[1]; tot, inf = v('ai_data_training_and_inference', 2025), v('ai_data_inference', 2025) |
| a.bar([0], [tot - inf], color=BLUE, width=.55, label='training'); a.bar([0], [inf], bottom=[tot - inf], color=RED, width=.55, label='inference') |
| a.text(0, (tot - inf) / 2, f'training\n{tot - inf:.1f}', ha='center', va='center', color=PAPER, fontsize=8.5) |
| a.text(0, tot - inf + inf / 2, f'inference\n{inf:.1f}', ha='center', va='center', color=PAPER, fontsize=8.5) |
| a.set_xlim(-.6, .6); a.set_xticks([]); a.set_yticks([]); clean(a, left=False) |
| a.set_title('Data used for AI, 2025\n(exabytes)', fontsize=9.5, loc='left') |
| a = axs[2]; m, e = v('data_market_trading', 2024), v('data_exchange_trading', 2024) |
| a.barh([1, 0], [m, e], color=[INK, BLUE], height=.55) |
| a.text(m, 1, f' {m / 10:,.0f} bn+', va='center', fontsize=9); a.text(e, 0, f' {e / 10:,.0f} bn+', va='center', fontsize=9) |
| a.set_yticks([1, 0]); a.set_yticklabels(['All data\ntrading', 'On data\nexchanges'], fontsize=8.5); a.set_xlim(0, 2300); a.set_xticks([]); clean(a, left=False) |
| a.set_title('Data trading, 2024\n(yuan)', fontsize=9.5, loc='left') |
| a = axs[3]; parts = [('Intangible\nassets', v('data_assets_booked_intangible', 2025), BLUE), ('Development\nspending', v('data_assets_booked_development', 2025), MID), |
| ('Inventory', v('data_assets_booked_inventory', 2025), LIGHT)] |
| left = 0 |
| for nm, val, c in parts: |
| a.barh(0, val, left=left, color=c, height=.5) |
| if val > 2: a.text(left + val / 2, 0, f'{val:.1f}', ha='center', va='center', color=PAPER, fontsize=8.5) |
| if val > 2: a.text(left + val / 2, -.42, nm, ha='center', va='top', fontsize=7.5, color=MID) |
| left += val |
| a.set_xlim(0, 40); a.set_ylim(-1, .6); a.set_xticks([]); a.set_yticks([]); clean(a, left=False) |
| a.set_title(f'Data booked as assets by\n{int(v("listed_firms_booking_data_assets", 2025))} listed firms, 2025 (100m yuan)', fontsize=9.5, loc='left') |
| a.text(40, .45, 'inventory 0.2', ha='right', fontsize=7.5, color=MID) |
| title(fig, 'Data in numbers', 'Data produced each year, data used for AI, the data market and data on company balance sheets. China produced about 27% of the world\'s data in 2025.', y=.96) |
| fig.text(.05, .03, 'Data: National Data Administration (national data resources surveys 2023 to 2025; national data work conference, January 2025); SAIF data asset tracking report via Yicai (June 2026). ' |
| '"+" marks lower bounds ("over"). Surveys started in 2023, so there is no earlier comparable series.', fontsize=7, color=MID) |
| plt.subplots_adjust(left=.05, right=.98, top=.74, bottom=.14) |
| fig.savefig('out/assets/data_in_numbers.png', facecolor=PAPER); plt.close(fig) |
|
|
| |
| fig = plt.figure(figsize=(13, 6.2)) |
| a1 = fig.add_axes([.05, .12, .36, .62]); a2 = fig.add_axes([.47, .06, .5, .78]) |
| yrs = comp.year.values |
| a1.bar(yrs - .18, comp.teams_registered / 1e3, width=.34, color=INK, label='Teams registered') |
| a1.bar(yrs + .18, comp.participants / 1e3, width=.34, color=RED, label='Participants') |
| for y, t, p_, c in zip(yrs, comp.teams_registered, comp.participants, comp.final_city): |
| a1.text(y - .18, t / 1e3 + 2, f'{t / 1e3:.0f}k', ha='center', fontsize=8.5) |
| a1.text(y + .18, p_ / 1e3 + 2, f'{p_ / 1e3:.0f}k', ha='center', fontsize=8.5, color=RED) |
| a1.text(y, -12, f'final: {c}', ha='center', fontsize=8, color=MID) |
| a1.set_xticks(yrs); a1.set_ylim(0, 165); a1.set_yticks([]); clean(a1, left=False); a1.legend(frameon=False, fontsize=8.5, loc='upper left') |
| a1.set_title('The "Data Element ×" competition', fontsize=10, loc='left') |
| EXT = (97, 128, 18, 43) |
| clip = box(EXT[0] - 2, EXT[2] - 2, EXT[1] + 2, EXT[3] + 2) |
| for f in json.load(open('raw/ne_50m_admin_0_countries.geojson'))['features']: |
| if f['properties']['ADM0_A3'] == 'CHN': continue |
| g = shape(f['geometry']) |
| if not g.intersects(clip): continue |
| for p in getattr(g.intersection(clip), 'geoms', [g.intersection(clip)]): |
| if p.geom_type == 'Polygon': a2.add_patch(MPoly(np.array(p.exterior.coords), closed=True, fc='#F5F3EF', ec='#E2DFD9', lw=.4)) |
| for f in json.load(open('raw/china_provinces.geojson'))['features']: |
| g = shape(f['geometry']) |
| for p in getattr(g, 'geoms', [g]): a2.add_patch(MPoly(np.array(p.exterior.coords), closed=True, fc=LAND, ec=PAPER, lw=.6)) |
| for c, gp in pl.groupby('city'): |
| la, lo = gp.latitude.iloc[0], gp.longitude.iloc[0] |
| kinds = gp.kind.tolist() |
| if 'Exchange' in kinds: a2.scatter(lo, la, s=90, marker='o', c=BLUE, ec=PAPER, lw=.8, zorder=4) |
| if 'Competition' in kinds: a2.scatter(lo, la, s=200, marker='o', fc='none', ec=RED, lw=1.6, zorder=5) |
| lab = '\n'.join(f'{r.year} {"exchange" if r.kind == "Exchange" else "competition final"}' for _, r in gp.sort_values('year').iterrows()) |
| dx, ha = (-10, 'right') if c in ('Guangzhou', 'Guiyang') else (10, 'left') |
| a2.annotate(f'{c}\n{lab}', (lo, la), xytext=(dx, 0), textcoords='offset points', ha=ha, va='center', fontsize=7.8, zorder=6, linespacing=1.2) |
| a2.set_xlim(EXT[0], EXT[1]); a2.set_ylim(EXT[2], EXT[3]); a2.set_aspect(1 / np.cos(np.radians(30))) |
| a2.set_xticks([]); a2.set_yticks([]); [s_.set_visible(False) for s_ in a2.spines.values()] |
| a2.legend(handles=[Line2D([], [], marker='o', ls='', color=BLUE, ms=8, label='Data exchange (selection)'), |
| Line2D([], [], marker='o', ls='', mfc='none', mec=RED, mew=1.6, ms=11, label='Competition final')], loc='lower right', frameon=False, fontsize=8.5) |
| title(fig, 'A national contest and a few exchanges', 'Registrations to the "Data Element ×" competition grew by half in two years; the first data exchanges opened in Guiyang, Beijing, Shanghai and the Greater Bay Area.', y=.96) |
| fig.text(.05, .015, 'Data: National Data Administration and People\'s Daily; figures are lower bounds ("over"), except 2024 participants ("nearly 100,000"). Exchanges: the five main ones by opening date, not a complete list. Borders: Natural Earth.', fontsize=7, color=MID) |
| fig.savefig('out/assets/competition_and_exchanges.png', facecolor=PAPER); plt.close(fig) |
|
|
| |
| tg = pd.read_parquet('out/data/targets_2030.parquet') |
| pj = pd.read_parquet('out/data/projections.parquet') |
| fig = plt.figure(figsize=(14, 9.4)) |
| a1 = fig.add_axes([.05, .5, .37, .34]); a2 = fig.add_axes([.58, .5, .36, .34]); a3 = fig.add_axes([.05, .09, .91, .27]) |
|
|
| yrs = [2023, 2024, 2025] |
| a1.bar(yrs, [v('data_production', y) for y in yrs], color=LIGHT, width=.55) |
| for y in yrs: a1.text(y, v('data_production', y) + 3, f"{v('data_production', y):.0f}", ha='center', fontsize=8.5) |
| for (name, g), c, lab in zip(pj[pj.indicator == 'data_production'].groupby('scenario', sort=False), [BLUE_SOFT, BLUE, RED], ['at 15% a year', 'at 20% a year', 'at the 2025 pace (27%)']): |
| a1.plot(g.year, g.value, color=c, lw=2, marker='o', ms=3) |
| a1.text(2030.2, g.value.iloc[-1], f"{g.value.iloc[-1]:.0f} ZB\n{lab}", va='center', fontsize=8, color=c if c != BLUE_SOFT else MID, linespacing=1.1) |
| a1.set_xlim(2022.4, 2030.1); a1.set_ylim(0, 190); a1.set_xticks(range(2023, 2031)); clean(a1); a1.yaxis.grid(color='#F2F2F2', lw=.6); a1.set_axisbelow(True) |
| a1.set_title('Data produced in China: what constant growth would give by 2030\n(zettabytes; bars: surveys, lines: arithmetic scenarios)', fontsize=9.5, loc='left') |
|
|
| for name, c, ls in [('target floor, 15% a year from 2024', INK, '--'), ('plan ambition, 20% a year to 2026', RED, ':')]: |
| g = pj[(pj.indicator == 'data_industry_scale') & (pj.scenario == name)] |
| a2.plot(g.year, g.value, color=c, lw=1.6, ls=ls) |
| a2.text(g.year.iloc[-1] + .12, g.value.iloc[-1], f"{g.value.iloc[-1]:.1f} {name.split(',')[0]}", va='center', fontsize=8, color=c) |
| act = [(y, v('data_industry_scale', y)) for y in (2024, 2025)] |
| a2.plot([a for a, _ in act], [b for _, b in act], color=BLUE, lw=2.4, marker='o', ms=6, zorder=4) |
| for y, val in act: a2.text(y - .08, val + .3, f'{val:.2f}', ha='right', fontsize=8.5, color=BLUE) |
| a2.text(2025.15, v('data_industry_scale', 2025) - .55, f"+{v('data_industry_growth', 2025):.1f}% in 2025:\nabove the 15% floor,\nbelow the 20% ambition", fontsize=8, color=BLUE, va='top', linespacing=1.15) |
| a2.set_xlim(2023.6, 2030.6); a2.set_ylim(4, 13); a2.set_xticks(range(2024, 2030)); clean(a2); a2.yaxis.grid(color='#F2F2F2', lw=.6); a2.set_axisbelow(True) |
| a2.set_title('Data industry against its targets\n(trillion yuan; blue: reported, lines: paths implied by the targets)', fontsize=9.5, loc='left') |
|
|
| a3.axhline(0, color=LIGHT, lw=1.2, zorder=1) |
| AREA = {'Data market': RED, 'AI': BLUE, 'Infrastructure': INK, 'Public data': MID, 'Digital economy': RED} |
| ys = sorted(tg.target_year.unique()) |
| xs = {y: i for i, y in enumerate(ys)} |
| for y, g in tg.groupby('target_year'): |
| x = xs[y] |
| a3.scatter(x, 0, s=60, color=RED if y <= 2030 else MID, zorder=3) |
| a3.text(x, .14, str(y), ha='center', fontsize=11, weight='bold') |
| for k, (_, r) in enumerate(g.iterrows()): |
| a3.text(x, -.16 - k * .42, f"{r.area}\n{textwrap.fill(r.target, 30)}", ha='center', va='top', fontsize=7.6, color=AREA.get(r.area, INK), linespacing=1.15) |
| a3.set_xlim(-.6, len(ys) - .4); a3.set_ylim(-1.15, .3); a3.axis('off') |
| fig.text(.05, .395, 'Official targets, as published', fontsize=10, weight='bold') |
| title(fig, 'Road to 2030', 'Where the data economy is heading if recent growth holds, and what the State has committed to. Scenarios are arithmetic, not forecasts.', y=.96) |
| fig.text(.05, .015, 'Data: National Data Administration surveys; NDDRI via China Daily (2024) and Xinhua (2025) for the data industry; targets from the State Council, NDRC, NDA, MIIT and the 15th Five-Year Plan outline. ' |
| '\nScenarios apply constant growth to the last reported value; they ignore saturation, definition changes and revisions.', fontsize=7, color=MID, va='bottom') |
| fig.savefig('out/assets/vision_2030.png', facecolor=PAPER); plt.close(fig) |
| print(sorted(os.listdir('out/assets'))) |
|
|
