Read the six folded datasets from data-lectures, not high_dim_data (#832)

Seven reads across four files. The six datasets moved into
QuantEcon/data-lectures (dl#62), so every read follows them -- and the
HOST changes too, not just the org and repo.

media.githubusercontent.com is the LFS *media* endpoint and routes per
path by LFS status. These six were LFS-tracked in high_dim_data and are
plain git in data-lectures, so the media host 404s for all of them.
Measured: the media URL for data-lectures returns 404 with 0 bytes, the
raw URL returns 200. A mechanical org/repo swap preserving the host
would have broken every chart in these three lectures.

All seven land on raw.githubusercontent.com, including any that used the
github.com/*/raw/ redirect form and would have survived a bare org swap.
One spelling across all four consuming repos is deliberate: the set 1/2
repoints broke lecture-wasm by "harmonising" its URLs onto the redirect
form, whose 302 carries an empty access-control-allow-origin and so is
rejected by the browser before it is followed (dl#46). If every repo
spells these lines the same way, that class of fix cannot recur.

_static/lecture_specific/inequality/data.ipynb is edited by hand. No
audit scans _static/**, the build never executes it, and the translation
sync is .md-only -- so nothing mechanical would ever carry this change.
It is also served: it returns 200 on the published site with the URL in
its body.

No prose is touched. The lecture's claim that the Forbes billionaires
data is a 2020 snapshot is contradicted by the bytes (282 timestamps
spanning 2020-04-07 to 2023-04-15, and the chart plots realTimeWorth
rather than the annual list), but that is content, not a repoint --
split out to QuantEcon/workspace-lectures#35. The Global 2000 "2020"
claim nearby IS correct and is deliberately left alone.

Verified: both acceptance greps clean (no media-host data-lectures read,
no high_dim_data reference anywhere in lectures/); all 7 URLs return 200;
three-way byte identity on the four cross_section files -- manifest
sha256 = new URL = old high_dim_data URL, so this changes the address and
not the data.

Part of QuantEcon/workspace-lectures#23. Data: QuantEcon/data-lectures#62

Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>

Author: mmcky

lectures/_static/lecture_specific/inequality/data.ipynb

@@ -34,7 +34,7 @@
     "import wbgapi as wb\n",
     "import plotly.express as px\n",
     "\n",
-    "url = 'https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main/SCF_plus/SCF_plus_mini.csv'\n",
+    "url = 'https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/SCF_plus_mini.csv'\n",
     "df = pd.read_csv(url)\n",
     "df_income_wealth = df.dropna()"
    ]

lectures/heavy_tails.md

@@ -824,7 +824,7 @@ mystnb:
     name: firm-size-dist
 tags: [hide-input]
 ---
-df_fs = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main/cross_section/forbes-global2000.csv')
+df_fs = pd.read_csv('https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/forbes-global2000.csv')
 df_fs = df_fs[['Country', 'Sales', 'Profits', 'Assets', 'Market Value']]
 fig, ax = plt.subplots(figsize=(6.4, 3.5))
 
@@ -851,8 +851,8 @@ mystnb:
 tags: [hide-input]
 ---
 # import population data of cities in 2023 United States and 2023 Brazil from world population review
-df_cs_us = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main/cross_section/cities_us.csv')
-df_cs_br = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main/cross_section/cities_brazil.csv')
+df_cs_us = pd.read_csv('https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/cities_us.csv')
+df_cs_br = pd.read_csv('https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/cities_brazil.csv')
 
 fig, axes = plt.subplots(1, 2, figsize=(8.8, 3.6))
 
@@ -876,7 +876,7 @@ mystnb:
     name: wealth-dist
 tags: [hide-input]
 ---
-df_w = pd.read_csv('https://media.githubusercontent.com/media/QuantEcon/high_dim_data/main/cross_section/forbes-billionaires.csv')
+df_w = pd.read_csv('https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/forbes-billionaires.csv')
 df_w = df_w[['country', 'realTimeWorth', 'realTimeRank']].dropna()
 df_w = df_w.astype({'realTimeRank': int})
 df_w = df_w.sort_values('realTimeRank', ascending=True).copy()

lectures/inequality.md

@@ -246,7 +246,7 @@ The following code block imports a subset of the dataset `SCF_plus` for 2016,
 which is derived from the [Survey of Consumer Finances](https://en.wikipedia.org/wiki/Survey_of_Consumer_Finances) (SCF).
 
 ```{code-cell} ipython3
-url = 'https://github.com/QuantEcon/high_dim_data/raw/main/SCF_plus/SCF_plus_mini.csv'
+url = 'https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/SCF_plus_mini.csv'
 df = pd.read_csv(url)
 df_income_wealth = df.dropna()
 ```

lectures/mle.md

@@ -90,7 +90,7 @@ The following code imports this data  and reads it into an array called `sample`
 ```{code-cell} ipython3
 :tags: [hide-input]
 
-url = 'https://github.com/QuantEcon/high_dim_data/raw/main/SCF_plus/SCF_plus_mini_no_weights.csv'
+url = 'https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/SCF_plus_mini_no_weights.csv'
 df = pd.read_csv(url)
 df = df.dropna()
 df = df[df['year'] == 2016]