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thermograph/cities.json

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SEO: crawlable programmatic climate pages + technical hygiene (#96) * SEO: generate curated city set for crawlable climate pages gen_cities.py reuses the GeoNames index places.py already parses to select the top ~500 metros by population, assigns each a stable URL-safe slug (dropping admin1 when it repeats the city name), and writes committed backend/cities.json. cities.py loads it lazily with slug lookup, all_slugs(), display_name(), and by_country() grouping for the upcoming hub + sitemap. * SEO: rendering core, robots.txt, sitemap.xml, and metadata hygiene - content.py: Jinja2 environment + HTML responder (ETag/304), dynamic /robots.txt (disallows /api and /alerts, points at the sitemap) and /sitemap.xml (enumerates the home/static pages plus every city, month, and records URL from cities.py). Registered on the app before the StaticFiles mount so the routes win. - templates/base.html.j2: shared layout with unique title/description, self- referential canonical, Open Graph, favicon/manifest, header nav (adds a Climate link) and a footer link graph. - Give each existing page a unique <meta description> (were 5x identical) and a self-referential <link rel=canonical>; add WebApplication JSON-LD to the home page. - Pin jinja2. * SEO: server-rendered per-city climate page (/climate/{slug}) The keystone crawlable page: for a city it snaps to the grid cell, loads the archive (fetching once if missing, self-healing), and renders as real HTML — a 'how today compares' block (grade + percentile per metric from grade_day, tinted by tier), a monthly normals table (climatology at each month's 15th, shown in °F and °C), all-time records (new grading.all_time_records helper), a breadcrumb, Dataset+Place+BreadcrumbList JSON-LD, self-referential canonical, and links into the interactive tool + month/records pages. Content-page CSS added to style.css (renamed the table class to avoid colliding with the app's .normals flex row). * SEO: month (/climate/{slug}/{month}) and records (/climate/{slug}/records) pages Month pages render the exact-month long-tail ('average weather in {city} in {month}') with that month's average high/low, typical p10-p90 range, month-specific records, and prev/next month links. Records pages show all-time record highs/lows per metric with dates (grading.all_time_records). Shared _resolve_city helper; the literal /records route is registered before the {month} param and month names are validated (unknown month -> 404). * SEO: climate hub, weather glossary, and about/methodology pages - /climate: crawlable directory of all ~500 cities grouped by country — the internal-link graph that lets search engines discover every city page. - /glossary + /glossary/{term}: plain-language definitions (climate normal, percentile, temperature anomaly, feels-like, heat index, wind chill, humidity, reanalysis) with DefinedTerm JSON-LD and cross-links into the tool. - /about: methodology page (ERA5 data source, 45-year baseline, +/-7-day window, percentile grading) for E-E-A-T. All linked from the shared footer. * SEO: archive warmer, content-page tests, and deploy docs - warm_cities.py: paced, idempotent offline warmer that pre-fetches each city cell's archive so /climate pages serve from cache and a crawl can't burst the archive quota (pages self-heal if hit before warming). - tests/test_content.py: city-set slug uniqueness/lookup, robots.txt, sitemap enumerating city/month/records URLs, and that a rendered city page carries the stats + canonical + Dataset JSON-LD in the HTML; plus month/records/hub/glossary/ about routing and 404s. - DEPLOY.md: document the content pages, the warm step, and submitting the sitemap.
2026-07-15 23:53:11 +00:00
[
{
"slug":"shanghai-cn",
"name":"Shanghai",
"admin1":"Shanghai",
"country":"China",
"country_code":"CN",
"lat":31.22222,
"lon":121.45806,
"population":24874500
},
{
"slug":"beijing-cn",
"name":"Beijing",
"admin1":"Beijing",
"country":"China",
"country_code":"CN",
"lat":39.9075,
"lon":116.39723,
"population":18960744
},
{
"slug":"shenzhen-guangdong-cn",
"name":"Shenzhen",
"admin1":"Guangdong",
"country":"China",
"country_code":"CN",
"lat":22.54554,
"lon":114.0683,
"population":17494398
},
{
"slug":"guangzhou-guangdong-cn",
"name":"Guangzhou",
"admin1":"Guangdong",
"country":"China",
"country_code":"CN",
"lat":23.11667,
"lon":113.25,
"population":16096724
},
{
"slug":"kinshasa-cd",
"name":"Kinshasa",
"admin1":"Kinshasa",
"country":"Democratic Republic of the Congo",
"country_code":"CD",
"lat":-4.32758,
"lon":15.31357,
"population":16000000
},
{
"slug":"istanbul-tr",
"name":"Istanbul",
"admin1":"Istanbul",
"country":"Turkey",
"country_code":"TR",
"lat":41.01384,
"lon":28.94966,
"population":15701602
},
{
"slug":"lagos-ng",
"name":"Lagos",
"admin1":"Lagos",
"country":"Nigeria",
"country_code":"NG",
"lat":6.45407,
"lon":3.39467,
"population":15388000
},
{
"slug":"ho-chi-minh-city-ho-chi-minh-city-hcmc-vn",
"name":"Ho Chi Minh City",
"admin1":"Ho Chi Minh City (HCMC)",
"country":"Vietnam",
"country_code":"VN",
"lat":10.82302,
"lon":106.62965,
"population":14002598
},
{
"slug":"chengdu-sichuan-cn",
"name":"Chengdu",
"admin1":"Sichuan",
"country":"China",
"country_code":"CN",
"lat":30.66667,
"lon":104.06667,
"population":13568357
},
{
"slug":"lahore-punjab-pk",
"name":"Lahore",
"admin1":"Punjab",
"country":"Pakistan",
"country_code":"PK",
"lat":31.558,
"lon":74.35071,
"population":13004135
},
{
"slug":"mumbai-maharashtra-in",
"name":"Mumbai",
"admin1":"Maharashtra",
"country":"India",
"country_code":"IN",
"lat":19.07283,
"lon":72.88261,
"population":12691836
},
{
"slug":"sao-paulo-br",
"name":"São Paulo",
"admin1":"São Paulo",
"country":"Brazil",
"country_code":"BR",
"lat":-23.5475,
"lon":-46.63611,
"population":12400232
},
{
"slug":"mexico-city-mx",
"name":"Mexico City",
"admin1":"Mexico City",
"country":"Mexico",
"country_code":"MX",
"lat":19.42847,
"lon":-99.12766,
"population":12294193
},
{
"slug":"karachi-sindh-pk",
"name":"Karachi",
"admin1":"Sindh",
"country":"Pakistan",
"country_code":"PK",
"lat":24.8608,
"lon":67.0104,
"population":11624219
},
{
"slug":"tianjin-cn",
"name":"Tianjin",
"admin1":"Tianjin",
"country":"China",
"country_code":"CN",
"lat":39.14222,
"lon":117.17667,
"population":11090314
},
{
"slug":"delhi-in",
"name":"Delhi",
"admin1":"Delhi",
"country":"India",
"country_code":"IN",
"lat":28.65195,
"lon":77.23149,
"population":11034555
},
{
"slug":"wuhan-hubei-cn",
"name":"Wuhan",
"admin1":"Hubei",
"country":"China",
"country_code":"CN",
"lat":30.58333,
"lon":114.26667,
"population":10392693
},
{
"slug":"moscow-ru",
"name":"Moscow",
"admin1":"Moscow",
"country":"Russia",
"country_code":"RU",
"lat":55.75204,
"lon":37.61781,
"population":10381222
},
{
"slug":"dhaka-dhaka-division-bd",
"name":"Dhaka",
"admin1":"Dhaka Division",
"country":"Bangladesh",
"country_code":"BD",
"lat":23.7104,
"lon":90.40744,
"population":10356500
},
{
"slug":"seoul-kr",
"name":"Seoul",
"admin1":"Seoul",
"country":"South Korea",
"country_code":"KR",
"lat":37.566,
"lon":126.9784,
"population":10349312
},
{
"slug":"tokyo-jp",
"name":"Tokyo",
"admin1":"Tokyo",
"country":"Japan",
"country_code":"JP",
"lat":35.6895,
"lon":139.69171,
"population":9733276
},
{
"slug":"dongguan-guangdong-cn",
"name":"Dongguan",
"admin1":"Guangdong",
"country":"China",
"country_code":"CN",
"lat":23.01797,
"lon":113.74866,
"population":9644871
},
{
"slug":"cairo-eg",
"name":"Cairo",
"admin1":"Cairo",
"country":"Egypt",
"country_code":"EG",
"lat":30.06263,
"lon":31.24967,
"population":9606916
},
{
"slug":"xian-shaanxi-cn",
"name":"Xi’an",
"admin1":"Shaanxi",
"country":"China",
"country_code":"CN",
"lat":34.25833,
"lon":108.92861,
"population":9600000
},
{
"slug":"johannesburg-gauteng-za",
"name":"Johannesburg",
"admin1":"Gauteng",
"country":"South Africa",
"country_code":"ZA",
"lat":-26.20227,
"lon":28.04363,
"population":9418183
},
{
"slug":"nanjing-jiangsu-cn",
"name":"Nanjing",
"admin1":"Jiangsu",
"country":"China",
"country_code":"CN",
"lat":32.06167,
"lon":118.77778,
"population":9314685
},
{
"slug":"hangzhou-zhejiang-cn",
"name":"Hangzhou",
"admin1":"Zhejiang",
"country":"China",
"country_code":"CN",
"lat":30.29365,
"lon":120.16142,
"population":9236032
},
{
"slug":"foshan-guangdong-cn",
"name":"Foshan",
"admin1":"Guangdong",
"country":"China",
"country_code":"CN",
"lat":23.02677,
"lon":113.13148,
"population":9042509
},
{
"slug":"london-england-gb",
"name":"London",
"admin1":"England",
"country":"United Kingdom",
"country_code":"GB",
"lat":51.50853,
"lon":-0.12574,
"population":8961989
},
{
"slug":"new-york-city-new-york-us",
"name":"New York City",
"admin1":"New York",
"country":"United States",
"country_code":"US",
"lat":40.71427,
"lon":-74.00597,
"population":8804190
},
{
"slug":"jakarta-id",
"name":"Jakarta",
"admin1":"Jakarta",
"country":"Indonesia",
"country_code":"ID",
"lat":-6.21462,
"lon":106.84513,
"population":8540121
},
{
"slug":"bengaluru-karnataka-in",
"name":"Bengaluru",
"admin1":"Karnataka",
"country":"India",
"country_code":"IN",
"lat":12.97194,
"lon":77.59369,
"population":8495492
},
{
"slug":"hanoi-vn",
"name":"Hanoi",
"admin1":"Hanoi",
"country":"Vietnam",
"country_code":"VN",
"lat":21.0245,
"lon":105.84117,
"population":8053663
},
{
"slug":"taipei-taiwan-tw",
"name":"Taipei",
"admin1":"Taiwan",
"country":"Taiwan",
"country_code":"TW",
"lat":25.05306,
"lon":121.52639,
"population":7871900
},
{
"slug":"lima-lima-province-pe",
"name":"Lima",
"admin1":"Lima Province",
"country":"Peru",
"country_code":"PE",
"lat":-12.04318,
"lon":-77.02824,
"population":7737002
},
{
"slug":"bogota-bogota-d-c-co",
"name":"Bogotá",
"admin1":"Bogota D.C.",
"country":"Colombia",
"country_code":"CO",
"lat":4.60971,
"lon":-74.08175,
"population":7674366
},
{
"slug":"chongqing-cn",
"name":"Chongqing",
"admin1":"Chongqing",
"country":"China",
"country_code":"CN",
"lat":29.56026,
"lon":106.55771,
"population":7457599
},
{
"slug":"hong-kong-hk",
"name":"Hong Kong",
"admin1":null,
"country":"Hong Kong",
"country_code":"HK",
"lat":22.27832,
"lon":114.17469,
"population":7396076
},
{
"slug":"baghdad-iq",
"name":"Baghdad",
"admin1":"Baghdad",
"country":"Iraq",
"country_code":"IQ",
"lat":33.34058,
"lon":44.40088,
"population":7216000
},
{
"slug":"wuzhong-ningxia-cn",
"name":"Wuzhong",
"admin1":"Ningxia",
"country":"China",
"country_code":"CN",
"lat":37.9867,
"lon":106.201,
"population":7202654
},
{
"slug":"qingdao-shandong-cn",
"name":"Qingdao",
"admin1":"Shandong",
"country":"China",
"country_code":"CN",
"lat":36.06488,
"lon":120.38042,
"population":7172451
},
{
"slug":"tehran-ir",
"name":"Tehran",
"admin1":"Tehran",
"country":"Iran",
"country_code":"IR",
"lat":35.69439,
"lon":51.42151,
"population":7153309
},
{
"slug":"shenyang-liaoning-cn",
"name":"Shenyang",
"admin1":"Liaoning",
"country":"China",
"country_code":"CN",
"lat":41.79222,
"lon":123.43278,
"population":7050000
},
{
"slug":"hyderabad-telangana-in",
"name":"Hyderabad",
"admin1":"Telangana",
"country":"India",
"country_code":"IN",
"lat":17.38405,
"lon":78.45636,
"population":6993262
},
{
"slug":"rio-de-janeiro-br",
"name":"Rio de Janeiro",
"admin1":"Rio de Janeiro",
"country":"Brazil",
"country_code":"BR",
"lat":-22.90642,
"lon":-43.18223,
"population":6747815
},
{
"slug":"suzhou-jiangsu-cn",
"name":"Suzhou",
"admin1":"Jiangsu",
"country":"China",
"country_code":"CN",
"lat":31.30408,
"lon":120.59538,
"population":6715559
},
{
"slug":"puxi-shanghai-cn",
"name":"Puxi",
"admin1":"Shanghai",
"country":"China",
"country_code":"CN",
"lat":31.24414,
"lon":121.46592,
"population":6683712
},
{
"slug":"ahmedabad-gujarat-in",
"name":"Ahmedabad",
"admin1":"Gujarat",
"country":"India",
"country_code":"IN",
"lat":23.02579,
"lon":72.58727,
"population":6357693
},
{
"slug":"abidjan-abidjan-autonomous-district-ci",
"name":"Abidjan",
"admin1":"Abidjan Autonomous District",
"country":"Ivory Coast",
"country_code":"CI",
"lat":5.35444,
"lon":-4.00167,
"population":6321017
},
{
"slug":"pudong-shanghai-cn",
"name":"Pudong",
"admin1":"Shanghai",
"country":"China",
"country_code":"CN",
"lat":31.23995,
"lon":121.50094,
"population":5681512
},
{
"slug":"sydney-new-south-wales-au",
"name":"Sydney",
"admin1":"New South Wales",
"country":"Australia",
"country_code":"AU",
"lat":-33.86785,
"lon":151.20732,
"population":5638830
},
{
"slug":"singapore-sg",
"name":"Singapore",
"admin1":null,
"country":"Singapore",
"country_code":"SG",
"lat":1.28967,
"lon":103.85007,
"population":5638700
},
{
"slug":"melbourne-victoria-au",
"name":"Melbourne",
"admin1":"Victoria",
"country":"Australia",
"country_code":"AU",
"lat":-37.814,
"lon":144.96332,
"population":5435590
},
{
"slug":"dar-es-salaam-dar-es-salaam-region-tz",
"name":"Dar es Salaam",
"admin1":"Dar es Salaam Region",
"country":"Tanzania",
"country_code":"TZ",
"lat":-6.82349,
"lon":39.26951,
"population":5383728
},
{
"slug":"saint-petersburg-st-petersburg-ru",
"name":"Saint Petersburg",
"admin1":"St.-Petersburg",
"country":"Russia",
"country_code":"RU",
"lat":59.93863,
"lon":30.31413,
"population":5351935
},
{
"slug":"alexandria-eg",
"name":"Alexandria",
"admin1":"Alexandria",
"country":"Egypt",
"country_code":"EG",
"lat":31.20176,
"lon":29.91582,
"population":5263542
},
{
"slug":"harbin-heilongjiang-cn",
"name":"Harbin",
"admin1":"Heilongjiang",
"country":"China",
"country_code":"CN",
"lat":45.75,
"lon":126.65,
"population":5242897
},
{
"slug":"bangkok-th",
"name":"Bangkok",
"admin1":"Bangkok",
"country":"Thailand",
"country_code":"TH",
"lat":13.75398,
"lon":100.50144,
"population":5104476
},
{
"slug":"hefei-anhui-cn",
"name":"Hefei",
"admin1":"Anhui",
"country":"China",
"country_code":"CN",
"lat":31.86389,
"lon":117.28083,
"population":5050000
},
{
"slug":"dalian-liaoning-cn",
"name":"Dalian",
"admin1":"Liaoning",
"country":"China",
"country_code":"CN",
"lat":38.91222,
"lon":121.60222,
"population":4913879
},
{
"slug":"kano-kano-state-ng",
"name":"Kano",
"admin1":"Kano State",
"country":"Nigeria",
"country_code":"NG",
"lat":12.00012,
"lon":8.51672,
"population":4910000
},
{
"slug":"santiago-santiago-metropolitan-cl",
"name":"Santiago",
"admin1":"Santiago Metropolitan",
"country":"Chile",
"country_code":"CL",
"lat":-33.45694,
"lon":-70.64827,
"population":4837295
},
{
"slug":"cape-town-western-cape-za",
"name":"Cape Town",
"admin1":"Western Cape",
"country":"South Africa",
"country_code":"ZA",
"lat":-33.92584,
"lon":18.42322,
"population":4772846
},
{
"slug":"peshawar-khyber-pakhtunkhwa-pk",
"name":"Peshawar",
"admin1":"Khyber Pakhtunkhwa",
"country":"Pakistan",
"country_code":"PK",
"lat":34.008,
"lon":71.57849,
"population":4758762
},
{
"slug":"changchun-jilin-cn",
"name":"Changchun",
"admin1":"Jilin",
"country":"China",
"country_code":"CN",
"lat":43.88,
"lon":125.32278,
"population":4714996
},
{
"slug":"jeddah-mecca-region-sa",
"name":"Jeddah",
"admin1":"Mecca Region",
"country":"Saudi Arabia",
"country_code":"SA",
"lat":21.49012,
"lon":39.18624,
"population":4697000
},
{
"slug":"chennai-tamil-nadu-in",
"name":"Chennai",
"admin1":"Tamil Nadu",
"country":"India",
"country_code":"IN",
"lat":13.08784,
"lon":80.27847,
"population":4681087
},
{
"slug":"kolkata-west-bengal-in",
"name":"Kolkata",
"admin1":"West Bengal",
"country":"India",
"country_code":"IN",
"lat":22.56263,
"lon":88.36304,
"population":4631392
},
{
"slug":"xiamen-fujian-cn",
"name":"Xiamen",
"admin1":"Fujian",
"country":"China",
"country_code":"CN",
"lat":24.47979,
"lon":118.08187,
"population":4617251
},
{
"slug":"surat-gujarat-in",
"name":"Surat",
"admin1":"Gujarat",
"country":"India",
"country_code":"IN",
"lat":21.19594,
"lon":72.83023,
"population":4591246
},
{
"slug":"yangon-mm",
"name":"Yangon",
"admin1":"Yangon",
"country":"Myanmar",
"country_code":"MM",
"lat":16.80528,
"lon":96.15611,
"population":4477638
},
{
"slug":"bao-an-guangdong-cn",
"name":"Bao'an",
"admin1":"Guangdong",
"country":"China",
"country_code":"CN",
"lat":22.55213,
"lon":113.88288,
"population":4476554
},
{
"slug":"kabul-af",
"name":"Kabul",
"admin1":"Kabul",
"country":"Afghanistan",
"country_code":"AF",
"lat":34.52813,
"lon":69.17233,
"population":4434550
},
{
"slug":"nairobi-nairobi-county-ke",
"name":"Nairobi",
"admin1":"Nairobi County",
"country":"Kenya",
"country_code":"KE",
"lat":-1.28333,
"lon":36.81667,
"population":4397073
},
{
"slug":"wuxi-jiangsu-cn",
"name":"Wuxi",
"admin1":"Jiangsu",
"country":"China",
"country_code":"CN",
"lat":31.56887,
"lon":120.28857,
"population":4396835
},
{
"slug":"giza-eg",
"name":"Giza",
"admin1":"Giza",
"country":"Egypt",
"country_code":"EG",
"lat":30.00944,
"lon":31.20861,
"population":4367343
},
{
"slug":"jinan-shandong-cn",
"name":"Jinan",
"admin1":"Shandong",
"country":"China",
"country_code":"CN",
"lat":36.66833,
"lon":116.99722,
"population":4335989
},
{
"slug":"taiyuan-shanxi-cn",
"name":"Taiyuan",
"admin1":"Shanxi",
"country":"China",
"country_code":"CN",
"lat":37.86944,
"lon":112.56028,
"population":4303673
},
{
"slug":"zhengzhou-henan-cn",
"name":"Zhengzhou",
"admin1":"Henan",
"country":"China",
"country_code":"CN",
"lat":34.75778,
"lon":113.64861,
"population":4253913
},
{
"slug":"bamako-ml",
"name":"Bamako",
"admin1":"Bamako",
"country":"Mali",
"country_code":"ML",
"lat":12.60915,
"lon":-7.97522,
"population":4227569
},
{
"slug":"riyadh-riyadh-region-sa",
"name":"Riyadh",
"admin1":"Riyadh Region",
"country":"Saudi Arabia",
"country_code":"SA",
"lat":24.68773,
"lon":46.72185,
"population":4205961
},
{
"slug":"new-taipei-city-taipei-tw",
"name":"New Taipei City",
"admin1":"Taipei",
"country":"Taiwan",
"country_code":"TW",
"lat":25.06199,
"lon":121.45703,
"population":4004367
},
{
"slug":"new-territories-hk",
"name":"New Territories",
"admin1":null,
"country":"Hong Kong",
"country_code":"HK",
"lat":22.42441,
"lon":114.11095,
"population":3984077
},
{
"slug":"shijiazhuang-hebei-cn",
"name":"Shijiazhuang",
"admin1":"Hebei",
"country":"China",
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SEO: add 250 English-market city pages; auto-warm archives on deploy (#97) The population-ranked global top-500 skewed to Asian megacities and missed high-English-search-demand cities. gen_cities.py now tops up with the top ~250 cities from English-speaking countries (US/GB/CA/AU/NZ/IE/ZA) not already in the global set, so US coverage goes 13->146, GB 2->42, CA 3->29, etc. (Seattle, Boston, Manchester, Melbourne, Auckland, Dublin, ...). cities.json regenerated to 750. Both deploy scripts now launch warm_cities.py automatically after the health check, detached (dev: a systemd --user transient unit; prod: setsid/nohup), so the city pages serve from cache without a manual step; idempotent, so only the first deploy does the full warm. DEPLOY.md updated.
2026-07-16 00:11:14 +00:00
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SEO: expand city set to 1000 (extended English-speaking / high-proficiency) (#99) Add a third tier to gen_cities.py: the top-population cities from the remaining English-official countries plus countries where >35% speak English (Eurobarometer/ EF), that weren't already chosen. cities.json grows to 1000 (500 global + 250 core-English + 250 extended), pulling in the biggest cities of India, Nigeria, Pakistan, the Philippines, plus Amsterdam/Stockholm/Nairobi/Tel Aviv, etc. gen_flavor.py is now incremental (fetches only cities missing a blurb, prunes stale ones; --full to rebuild); cities_flavor.json refreshed to cover 942/1000.
2026-07-16 01:14:30 +00:00
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"slug":"belagavi-karnataka-in",
"name":"Belagavi",
"admin1":"Karnataka",
"country":"India",
"country_code":"IN",
"lat":15.85212,
"lon":74.50447,
"population":490045
},
{
"slug":"saharanpur-uttar-pradesh-in",
"name":"Sahāranpur",
"admin1":"Uttar Pradesh",
"country":"India",
"country_code":"IN",
"lat":29.9679,
"lon":77.54522,
"population":484873
},
{
"slug":"vellore-tamil-nadu-in",
"name":"Vellore",
"admin1":"Tamil Nadu",
"country":"India",
"country_code":"IN",
"lat":12.9184,
"lon":79.13255,
"population":484690
},
{
"slug":"angeles-city-central-luzon-ph",
"name":"Angeles City",
"admin1":"Central Luzon",
"country":"Philippines",
"country_code":"PH",
"lat":15.15,
"lon":120.58333,
"population":483452
},
{
"slug":"bhatpara-west-bengal-in",
"name":"Bhātpāra",
"admin1":"West Bengal",
"country":"India",
"country_code":"IN",
"lat":22.86643,
"lon":88.40113,
"population":483129
},
{
"slug":"imus-calabarzon-ph",
"name":"Imus",
"admin1":"Calabarzon",
"country":"Philippines",
"country_code":"PH",
"lat":14.42972,
"lon":120.93667,
"population":481949
},
{
"slug":"malegaon-maharashtra-in",
"name":"Malegaon",
"admin1":"Maharashtra",
"country":"India",
"country_code":"IN",
"lat":20.54966,
"lon":74.53462,
"population":481228
},
{
"slug":"okene-kogi-state-ng",
"name":"Okene",
"admin1":"Kogi State",
"country":"Nigeria",
"country_code":"NG",
"lat":7.55122,
"lon":6.23589,
"population":479178
},
{
"slug":"eldoret-uasin-gishu-county-ke",
"name":"Eldoret",
"admin1":"Uasin Gishu County",
"country":"Kenya",
"country_code":"KE",
"lat":0.52036,
"lon":35.26993,
"population":475716
},
{
"slug":"the-hague-south-holland-nl",
"name":"The Hague",
"admin1":"South Holland",
"country":"The Netherlands",
"country_code":"NL",
"lat":52.07667,
"lon":4.29861,
"population":474292
},
{
"slug":"gaya-bihar-in",
"name":"Gaya",
"admin1":"Bihar",
"country":"India",
"country_code":"IN",
"lat":24.79686,
"lon":85.00385,
"population":474093
},
{
"slug":"iloilo-western-visayas-ph",
"name":"Iloilo",
"admin1":"Western Visayas",
"country":"Philippines",
"country_code":"PH",
"lat":10.69694,
"lon":122.56444,
"population":473728
},
{
"slug":"morogoro-tz",
"name":"Morogoro",
"admin1":"Morogoro",
"country":"Tanzania",
"country_code":"TZ",
"lat":-6.82102,
"lon":37.66122,
"population":471409
},
{
"slug":"marikina-city-national-capital-region-ph",
"name":"Marikina City",
"admin1":"National Capital Region",
"country":"Philippines",
"country_code":"PH",
"lat":14.6481,
"lon":121.1133,
"population":471323
},
{
"slug":"ambattur-tamil-nadu-in",
"name":"Ambattur",
"admin1":"Tamil Nadu",
"country":"India",
"country_code":"IN",
"lat":13.09818,
"lon":80.16152,
"population":466205
},
{
"slug":"mandaluyong-city-national-capital-region-ph",
"name":"Mandaluyong City",
"admin1":"National Capital Region",
"country":"Philippines",
"country_code":"PH",
"lat":14.5832,
"lon":121.0409,
"population":465902
},
{
"slug":"ikare-ondo-state-ng",
"name":"Ikare",
"admin1":"Ondo State",
"country":"Nigeria",
"country_code":"NG",
"lat":7.52591,
"lon":5.75342,
"population":465000
},
{
"slug":"tamale-northern-gh",
"name":"Tamale",
"admin1":"Northern",
"country":"Ghana",
"country_code":"GH",
"lat":9.40079,
"lon":-0.8393,
"population":464316
},
{
"slug":"kira-central-region-ug",
"name":"Kira",
"admin1":"Central Region",
"country":"Uganda",
"country_code":"UG",
"lat":0.4,
"lon":32.63333,
"population":462900
},
{
"slug":"jalgaon-maharashtra-in",
"name":"Jalgaon",
"admin1":"Maharashtra",
"country":"India",
"country_code":"IN",
"lat":21.00292,
"lon":75.56602,
"population":460228
},
{
"slug":"kurnool-andhra-pradesh-in",
"name":"Kurnool",
"admin1":"Andhra Pradesh",
"country":"India",
"country_code":"IN",
"lat":15.82887,
"lon":78.03602,
"population":460184
},
{
"slug":"yola-adamawa-ng",
"name":"Yola",
"admin1":"Adamawa",
"country":"Nigeria",
"country_code":"NG",
"lat":9.20839,
"lon":12.48146,
"population":460000
},
{
"slug":"bacolod-city-western-visayas-ph",
"name":"Bacolod City",
"admin1":"Western Visayas",
"country":"Philippines",
"country_code":"PH",
"lat":10.66667,
"lon":122.95,
"population":454898
},
{
"slug":"mansilingan-western-visayas-ph",
"name":"Mansilingan",
"admin1":"Western Visayas",
"country":"Philippines",
"country_code":"PH",
"lat":10.63111,
"lon":122.97889,
"population":454150
},
{
"slug":"kahama-shinyanga-tz",
"name":"Kahama",
"admin1":"Shinyanga",
"country":"Tanzania",
"country_code":"TZ",
"lat":-3.83333,
"lon":32.6,
"population":453654
},
{
"slug":"ramgundam-telangana-in",
"name":"Rāmgundam",
"admin1":"Telangana",
"country":"India",
"country_code":"IN",
"lat":18.80084,
"lon":79.45206,
"population":452261
},
{
"slug":"udaipur-rajasthan-in",
"name":"Udaipur",
"admin1":"Rajasthan",
"country":"India",
"country_code":"IN",
"lat":24.58584,
"lon":73.71346,
"population":451100
},
{
"slug":"juba-central-equatoria-ss",
"name":"Juba",
"admin1":"Central Equatoria",
"country":"South Sudan",
"country_code":"SS",
"lat":4.85165,
"lon":31.58247,
"population":450000
},
{
"slug":"maheshtala-west-bengal-in",
"name":"Maheshtala",
"admin1":"West Bengal",
"country":"India",
"country_code":"IN",
"lat":22.50862,
"lon":88.25322,
"population":448317
},
{
"slug":"patiala-punjab-in",
"name":"Patiāla",
"admin1":"Punjab",
"country":"India",
"country_code":"IN",
"lat":30.33625,
"lon":76.3922,
"population":446246
},
{
"slug":"shyamnagar-west-bengal-in",
"name":"Shyamnagar",
"admin1":"West Bengal",
"country":"India",
"country_code":"IN",
"lat":22.83333,
"lon":88.36667,
"population":441956
},
{
"slug":"dasmarinas-calabarzon-ph",
"name":"Dasmariñas",
"admin1":"Calabarzon",
"country":"Philippines",
"country_code":"PH",
"lat":14.32944,
"lon":120.93667,
"population":441876
},
{
"slug":"sandakan-sabah-my",
"name":"Sandakan",
"admin1":"Sabah",
"country":"Malaysia",
"country_code":"MY",
"lat":5.8402,
"lon":118.1179,
"population":439050
},
{
"slug":"uyo-akwa-ibom-state-ng",
"name":"Uyo",
"admin1":"Akwa Ibom State",
"country":"Nigeria",
"country_code":"NG",
"lat":5.05127,
"lon":7.9335,
"population":436606
},
{
"slug":"davangere-karnataka-in",
"name":"Davangere",
"admin1":"Karnataka",
"country":"India",
"country_code":"IN",
"lat":14.46693,
"lon":75.92694,
"population":435128
},
{
"slug":"ado-ekiti-ekiti-state-ng",
"name":"Ado-Ekiti",
"admin1":"Ekiti State",
"country":"Nigeria",
"country_code":"NG",
"lat":7.62329,
"lon":5.22087,
"population":435000
},
{
"slug":"ogbomoso-oyo-state-ng",
"name":"Ogbomoso",
"admin1":"Oyo State",
"country":"Nigeria",
"country_code":"NG",
"lat":8.13373,
"lon":4.24014,
"population":433030
},
{
"slug":"tel-aviv-il",
"name":"Tel Aviv",
"admin1":"Tel Aviv",
"country":"Israel",
"country_code":"IL",
"lat":32.08088,
"lon":34.78057,
"population":432892
},
{
"slug":"sham-shui-po-sham-shui-po-district-hk",
"name":"Sham Shui Po",
"admin1":"Sham Shui Po District",
"country":"Hong Kong",
"country_code":"HK",
"lat":22.33023,
"lon":114.15945,
"population":431090
},
{
"slug":"akola-maharashtra-in",
"name":"Akola",
"admin1":"Maharashtra",
"country":"India",
"country_code":"IN",
"lat":20.70957,
"lon":76.9981,
"population":428857
},
{
"slug":"tando-bago-sindh-pk",
"name":"Tando Bago",
"admin1":"Sindh",
"country":"Pakistan",
"country_code":"PK",
"lat":24.78914,
"lon":68.96535,
"population":426535
},
{
"slug":"kuala-terengganu-terengganu-my",
"name":"Kuala Terengganu",
"admin1":"Terengganu",
"country":"Malaysia",
"country_code":"MY",
"lat":5.3302,
"lon":103.1408,
"population":426500
},
{
"slug":"wong-tai-sin-wong-tai-sin-district-hk",
"name":"Wong Tai Sin",
"admin1":"Wong Tai Sin District",
"country":"Hong Kong",
"country_code":"HK",
"lat":22.35,
"lon":114.18333,
"population":425235
},
{
"slug":"minna-niger-state-ng",
"name":"Minna",
"admin1":"Niger State",
"country":"Nigeria",
"country_code":"NG",
"lat":9.61524,
"lon":6.54776,
"population":425000
},
{
"slug":"mandaluyong-national-capital-region-ph",
"name":"Mandaluyong",
"admin1":"National Capital Region",
"country":"Philippines",
"country_code":"PH",
"lat":14.57837,
"lon":121.03522,
"population":425000
},
{
"slug":"rajpur-sonarpur-west-bengal-in",
"name":"Rajpur Sonarpur",
"admin1":"West Bengal",
"country":"India",
"country_code":"IN",
"lat":22.4382,
"lon":88.43205,
"population":424368
},
{
"slug":"taman-petaling-kuala-lumpur-my",
"name":"Taman Petaling",
"admin1":"Kuala Lumpur",
"country":"Malaysia",
"country_code":"MY",
"lat":3.19916,
"lon":101.64983,
"population":423062
},
{
"slug":"tando-allahyar-sindh-pk",
"name":"Tando Allahyar",
"admin1":"Sindh",
"country":"Pakistan",
"country_code":"PK",
"lat":25.4605,
"lon":68.71745,
"population":421923
},
{
"slug":"korba-chhattisgarh-in",
"name":"Korba",
"admin1":"Chhattisgarh",
"country":"India",
"country_code":"IN",
"lat":22.3458,
"lon":82.69633,
"population":419146
},
{
"slug":"kowloon-city-hk",
"name":"Kowloon City",
"admin1":"Kowloon City",
"country":"Hong Kong",
"country_code":"HK",
"lat":22.33047,
"lon":114.19222,
"population":418732
},
{
"slug":"alor-setar-kedah-my",
"name":"Alor Setar",
"admin1":"Kedah",
"country":"Malaysia",
"country_code":"MY",
"lat":6.12104,
"lon":100.36014,
"population":417800
},
{
"slug":"pasay-national-capital-region-ph",
"name":"Pasay",
"admin1":"National Capital Region",
"country":"Philippines",
"country_code":"PH",
"lat":14.53748,
"lon":121.00144,
"population":416522
},
{
"slug":"zurich-ch",
"name":"Zürich",
"admin1":"Zurich",
"country":"Switzerland",
"country_code":"CH",
"lat":47.36667,
"lon":8.55,
"population":415367
},
{
"slug":"bunamwaya-central-region-ug",
"name":"Bunamwaya",
"admin1":"Central Region",
"country":"Uganda",
"country_code":"UG",
"lat":0.25344,
"lon":32.55723,
"population":413400
},
{
"slug":"jhansi-uttar-pradesh-in",
"name":"Jhānsi",
"admin1":"Uttar Pradesh",
"country":"India",
"country_code":"IN",
"lat":25.45887,
"lon":78.57994,
"population":412927
},
{
"slug":"tseung-kwan-o-sai-kung-district-hk",
"name":"Tseung Kwan O",
"admin1":"Sai Kung District",
"country":"Hong Kong",
"country_code":"HK",
"lat":22.32789,
"lon":114.24992,
"population":412900
},
{
"slug":"kampung-kangkar-teberau-johor-my",
"name":"Kampung Kangkar Teberau",
"admin1":"Johor",
"country":"Malaysia",
"country_code":"MY",
"lat":1.532,
"lon":103.7549,
"population":412373
},
{
"slug":"wandsbek-hamburg-de",
"name":"Wandsbek",
"admin1":"Hamburg",
"country":"Germany",
"country_code":"DE",
"lat":53.58334,
"lon":10.08305,
"population":411422
},
{
"slug":"thoothukudi-tamil-nadu-in",
"name":"Thoothukudi",
"admin1":"Tamil Nadu",
"country":"India",
"country_code":"IN",
"lat":8.76735,
"lon":78.13425,
"population":410760
},
{
"slug":"ballari-karnataka-in",
"name":"Ballari",
"admin1":"Karnataka",
"country":"India",
"country_code":"IN",
"lat":15.14205,
"lon":76.92398,
"population":410445
},
{
"slug":"kuching-sarawak-my",
"name":"Kuching",
"admin1":"Sarawak",
"country":"Malaysia",
"country_code":"MY",
"lat":1.55,
"lon":110.33333,
"population":402738
},
{
"slug":"tarlac-city-central-luzon-ph",
"name":"Tarlac City",
"admin1":"Central Luzon",
"country":"Philippines",
"country_code":"PH",
"lat":15.48017,
"lon":120.59794,
"population":401892
},
{
"slug":"lekki-lagos-ng",
"name":"Lekki",
"admin1":"Lagos",
"country":"Nigeria",
"country_code":"NG",
"lat":6.44632,
"lon":3.47717,
"population":401272
},
{
"slug":"wah-cantt-punjab-pk",
"name":"Wah Cantt",
"admin1":"Punjab",
"country":"Pakistan",
"country_code":"PK",
"lat":33.77094,
"lon":72.75116,
"population":400733
},
{
"slug":"bhagalpur-bihar-in",
"name":"Bhāgalpur",
"admin1":"Bihar",
"country":"India",
"country_code":"IN",
"lat":25.24446,
"lon":86.97183,
"population":400146
},
{
"slug":"agartala-tripura-in",
"name":"Agartala",
"admin1":"Tripura",
"country":"India",
"country_code":"IN",
"lat":23.83605,
"lon":91.27939,
"population":400004
},
{
"slug":"west-jerusalem-jerusalem-il",
"name":"West Jerusalem",
"admin1":"Jerusalem",
"country":"Israel",
"country_code":"IL",
"lat":31.78199,
"lon":35.21961,
"population":400000
},
{
"slug":"bida-niger-state-ng",
"name":"Bida",
"admin1":"Niger State",
"country":"Nigeria",
"country_code":"NG",
"lat":9.08044,
"lon":6.0099,
"population":400000
},
{
"slug":"kisumu-kisumu-county-ke",
"name":"Kisumu",
"admin1":"Kisumu County",
"country":"Kenya",
"country_code":"KE",
"lat":-0.10221,
"lon":34.76171,
"population":397957
},
{
"slug":"tallinn-harjumaa-ee",
"name":"Tallinn",
"admin1":"Harjumaa",
"country":"Estonia",
"country_code":"EE",
"lat":59.43696,
"lon":24.75353,
"population":394024
SEO: crawlable programmatic climate pages + technical hygiene (#96) * SEO: generate curated city set for crawlable climate pages gen_cities.py reuses the GeoNames index places.py already parses to select the top ~500 metros by population, assigns each a stable URL-safe slug (dropping admin1 when it repeats the city name), and writes committed backend/cities.json. cities.py loads it lazily with slug lookup, all_slugs(), display_name(), and by_country() grouping for the upcoming hub + sitemap. * SEO: rendering core, robots.txt, sitemap.xml, and metadata hygiene - content.py: Jinja2 environment + HTML responder (ETag/304), dynamic /robots.txt (disallows /api and /alerts, points at the sitemap) and /sitemap.xml (enumerates the home/static pages plus every city, month, and records URL from cities.py). Registered on the app before the StaticFiles mount so the routes win. - templates/base.html.j2: shared layout with unique title/description, self- referential canonical, Open Graph, favicon/manifest, header nav (adds a Climate link) and a footer link graph. - Give each existing page a unique <meta description> (were 5x identical) and a self-referential <link rel=canonical>; add WebApplication JSON-LD to the home page. - Pin jinja2. * SEO: server-rendered per-city climate page (/climate/{slug}) The keystone crawlable page: for a city it snaps to the grid cell, loads the archive (fetching once if missing, self-healing), and renders as real HTML — a 'how today compares' block (grade + percentile per metric from grade_day, tinted by tier), a monthly normals table (climatology at each month's 15th, shown in °F and °C), all-time records (new grading.all_time_records helper), a breadcrumb, Dataset+Place+BreadcrumbList JSON-LD, self-referential canonical, and links into the interactive tool + month/records pages. Content-page CSS added to style.css (renamed the table class to avoid colliding with the app's .normals flex row). * SEO: month (/climate/{slug}/{month}) and records (/climate/{slug}/records) pages Month pages render the exact-month long-tail ('average weather in {city} in {month}') with that month's average high/low, typical p10-p90 range, month-specific records, and prev/next month links. Records pages show all-time record highs/lows per metric with dates (grading.all_time_records). Shared _resolve_city helper; the literal /records route is registered before the {month} param and month names are validated (unknown month -> 404). * SEO: climate hub, weather glossary, and about/methodology pages - /climate: crawlable directory of all ~500 cities grouped by country — the internal-link graph that lets search engines discover every city page. - /glossary + /glossary/{term}: plain-language definitions (climate normal, percentile, temperature anomaly, feels-like, heat index, wind chill, humidity, reanalysis) with DefinedTerm JSON-LD and cross-links into the tool. - /about: methodology page (ERA5 data source, 45-year baseline, +/-7-day window, percentile grading) for E-E-A-T. All linked from the shared footer. * SEO: archive warmer, content-page tests, and deploy docs - warm_cities.py: paced, idempotent offline warmer that pre-fetches each city cell's archive so /climate pages serve from cache and a crawl can't burst the archive quota (pages self-heal if hit before warming). - tests/test_content.py: city-set slug uniqueness/lookup, robots.txt, sitemap enumerating city/month/records URLs, and that a rendered city page carries the stats + canonical + Dataset JSON-LD in the HTML; plus month/records/hub/glossary/ about routing and 404s. - DEPLOY.md: document the content pages, the warm step, and submitting the sitemap.
2026-07-15 23:53:11 +00:00
}
]
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