Add POIs and journey times to map
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11 changed files with 914 additions and 177 deletions
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@ -10,7 +10,7 @@ import type {
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ApiResponse,
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POI,
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POIResponse,
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POICategoryGroup,
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POICategoriesMap,
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ColorMode,
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} from './types';
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@ -55,23 +55,30 @@ export default function App() {
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// POI state
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const [pois, setPois] = useState<POI[]>([]);
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const [selectedPOICategories, setSelectedPOICategories] = useState<Set<POICategoryGroup>>(
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new Set()
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);
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const [poiCategories, setPOICategories] = useState<POICategoriesMap>({});
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const [selectedPOICategories, setSelectedPOICategories] = useState<Set<string>>(new Set());
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const poiDebounceRef = useRef<ReturnType<typeof setTimeout> | null>(null);
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const poiAbortControllerRef = useRef<AbortController | null>(null);
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// Fetch POI category definitions from server on mount
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useEffect(() => {
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fetch(`${getApiBaseUrl()}/api/poi-categories`)
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.then((res) => res.json())
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.then((json: { categories: POICategoriesMap }) => {
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setPOICategories(json.categories);
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})
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.catch((err) => console.error('Failed to fetch POI categories:', err));
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}, []);
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// Debounced fetch when dependencies change
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useEffect(() => {
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if (!bounds) return;
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// Clear previous debounce timer
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if (debounceRef.current) {
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clearTimeout(debounceRef.current);
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}
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debounceRef.current = setTimeout(async () => {
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// Cancel any in-flight request
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if (abortControllerRef.current) {
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abortControllerRef.current.abort();
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}
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@ -167,6 +174,7 @@ export default function App() {
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filters={filters}
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onChange={setFilters}
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zoom={zoom}
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poiCategories={poiCategories}
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selectedPOICategories={selectedPOICategories}
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onPOICategoriesChange={setSelectedPOICategories}
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colorMode={colorMode}
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@ -1,32 +1,25 @@
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import { useState, useRef, useEffect } from 'react';
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import { Slider } from './ui/slider';
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import { Label } from './ui/label';
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import { YEAR_MIN, YEAR_MAX, YEAR_STEP, PRICE_MIN, PRICE_MAX, PRICE_STEP } from '../lib/constants';
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import type { Filters as FiltersType, POICategoryGroup, ColorMode } from '../types';
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import { POI_CATEGORY_GROUPS } from '../types';
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import type { Filters as FiltersType, POICategoriesMap, ColorMode } from '../types';
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interface FiltersProps {
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filters: FiltersType;
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onChange: (filters: FiltersType) => void;
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zoom: number;
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selectedPOICategories: Set<POICategoryGroup>;
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onPOICategoriesChange: (categories: Set<POICategoryGroup>) => void;
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poiCategories: POICategoriesMap;
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selectedPOICategories: Set<string>;
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onPOICategoriesChange: (categories: Set<string>) => void;
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colorMode: ColorMode;
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onColorModeChange: (mode: ColorMode) => void;
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}
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const POI_LABELS: Record<POICategoryGroup, string> = {
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schools: '🏫 Schools',
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healthcare: '🏥 Healthcare',
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transport: '🚉 Transport',
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parks: '🌳 Parks',
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emergency: '🚨 Emergency',
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supermarkets: '🛒 Supermarkets',
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};
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export default function Filters({
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filters,
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onChange,
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zoom,
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poiCategories,
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selectedPOICategories,
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onPOICategoriesChange,
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colorMode,
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@ -34,16 +27,41 @@ export default function Filters({
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}: FiltersProps) {
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const update = (key: keyof FiltersType, value: number) => onChange({ ...filters, [key]: value });
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const togglePOICategory = (category: POICategoryGroup) => {
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const [dropdownOpen, setDropdownOpen] = useState(false);
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const dropdownRef = useRef<HTMLDivElement>(null);
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// Close dropdown when clicking outside
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useEffect(() => {
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function handleClickOutside(event: MouseEvent) {
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if (dropdownRef.current && !dropdownRef.current.contains(event.target as Node)) {
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setDropdownOpen(false);
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}
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}
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document.addEventListener('mousedown', handleClickOutside);
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return () => document.removeEventListener('mousedown', handleClickOutside);
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}, []);
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const toggleCategory = (key: string) => {
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const newSet = new Set(selectedPOICategories);
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if (newSet.has(category)) {
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newSet.delete(category);
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if (newSet.has(key)) {
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newSet.delete(key);
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} else {
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newSet.add(category);
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newSet.add(key);
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}
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onPOICategoriesChange(newSet);
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};
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const selectAll = () => {
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onPOICategoriesChange(new Set(Object.keys(poiCategories)));
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};
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const selectNone = () => {
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onPOICategoriesChange(new Set());
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};
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const categoryKeys = Object.keys(poiCategories);
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const selectedCount = selectedPOICategories.size;
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return (
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<div className="w-72 p-4 bg-white shadow-lg space-y-6 overflow-y-auto max-h-screen">
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<h1 className="text-xl font-bold">UK Property Prices</h1>
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@ -139,22 +157,70 @@ export default function Filters({
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</div>
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)}
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<div className="space-y-2">
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<div className="space-y-2" ref={dropdownRef}>
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<Label>Points of Interest</Label>
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<div className="space-y-1">
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{POI_CATEGORY_GROUPS.map((category) => (
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<label key={category} className="flex items-center gap-2 cursor-pointer">
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<button
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onClick={() => setDropdownOpen(!dropdownOpen)}
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className="w-full flex items-center justify-between px-3 py-2 text-sm border border-slate-300 rounded hover:border-slate-400 bg-white"
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>
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<span className="truncate text-left">
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{selectedCount === 0
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? 'Select categories...'
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: selectedCount === categoryKeys.length
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? 'All categories'
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: `${selectedCount} selected`}
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</span>
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<svg
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className={`w-4 h-4 ml-2 flex-shrink-0 transition-transform ${dropdownOpen ? 'rotate-180' : ''}`}
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fill="none"
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stroke="currentColor"
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viewBox="0 0 24 24"
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>
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<path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M19 9l-7 7-7-7" />
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</svg>
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</button>
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{dropdownOpen && (
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<div className="border border-slate-300 rounded shadow-lg bg-white">
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<div className="flex gap-2 px-3 py-2 border-b border-slate-200">
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<button
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onClick={selectAll}
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className="text-xs text-blue-600 hover:text-blue-800"
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>
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All
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</button>
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<span className="text-xs text-slate-300">|</span>
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<button
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onClick={selectNone}
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className="text-xs text-blue-600 hover:text-blue-800"
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>
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None
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</button>
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</div>
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<div className="max-h-64 overflow-y-auto py-1">
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{categoryKeys.map((key) => {
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const { emoji, label } = poiCategories[key];
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return (
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<label
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key={key}
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className="flex items-center gap-2 px-3 py-1.5 hover:bg-slate-50 cursor-pointer"
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>
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<input
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type="checkbox"
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checked={selectedPOICategories.has(category)}
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onChange={() => togglePOICategory(category)}
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checked={selectedPOICategories.has(key)}
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onChange={() => toggleCategory(key)}
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className="rounded"
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/>
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<span className="text-sm">{POI_LABELS[category]}</span>
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<span className="text-sm">
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{emoji} {label}
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</span>
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</label>
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))}
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);
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})}
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</div>
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</div>
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)}
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</div>
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</div>
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);
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}
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@ -1,6 +1,7 @@
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import { useCallback, useRef, useEffect, useState, useMemo } from 'react';
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import { Map as MapGL } from 'react-map-gl/maplibre';
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import DeckGL from '@deck.gl/react';
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import { Map as MapGL, useControl } from 'react-map-gl/maplibre';
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import type { MapRef } from 'react-map-gl/maplibre';
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import { MapboxOverlay } from '@deck.gl/mapbox';
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import { H3HexagonLayer } from '@deck.gl/geo-layers';
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import { IconLayer } from '@deck.gl/layers';
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import type { PickingInfo } from '@deck.gl/core';
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@ -19,35 +20,119 @@ const TWEMOJI_BASE = 'https://cdn.jsdelivr.net/gh/twitter/twemoji@14.0.2/assets/
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// Map category to Twemoji codepoint (emoji unicode -> hex)
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const POI_EMOJI_CODES: Record<string, string> = {
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// Schools
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elementary_school: '1f3eb', // 🏫
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school: '1f3eb',
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high_school: '1f393', // 🎓
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// Education
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school: '1f3eb', // 🏫
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preschool: '1f476', // 👶
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college_university: '1f393',
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private_school: '1f3eb',
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college_university: '1f393', // 🎓
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library: '1f4da', // 📚
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// Healthcare
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doctor: '1f3e5', // 🏥
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dentist: '1f9b7', // 🦷
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pharmacy: '1f48a', // 💊
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hospital: '1f3e5',
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public_health_clinic: '1f3e5',
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veterinary: '1f43e', // 🐾
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nursing_home: '1f3e0', // 🏠
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social_facility: '1f91d', // 🤝
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// Transport
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train_station: '1f689', // 🚉
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bus_station: '1f68c', // 🚌
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bus_stop: '1f68f', // 🚏
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metro_station: '1f687', // 🚇
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light_rail_and_subway_stations: '1f687',
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// Parks
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light_rail_station: '1f687',
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tram_stop: '1f68a', // 🚊
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ferry_terminal: '26f4', // ⛴
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airport: '2708', // ✈
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// Parks & Leisure
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park: '1f333', // 🌳
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national_park: '1f3de', // 🏞
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nature_reserve: '1f33f', // 🌿
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dog_park: '1f415', // 🐕
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playground: '1f3a0', // 🎠
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garden: '1f33a', // 🌺
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sports_centre: '1f3c3', // 🏃
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swimming_pool: '1f3ca', // 🏊
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gym: '1f4aa', // 💪
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golf_course: '26f3', // ⛳
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marina: '26f5', // ⛵
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// Emergency
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police_department: '1f694', // 🚔
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fire_department: '1f692', // 🚒
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// Supermarkets
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// Supermarkets & Grocery
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supermarket: '1f6d2', // 🛒
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grocery_store: '1f6d2',
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convenience_store: '1f3ea', // 🏪
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bakery: '1f35e', // 🍞
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butcher: '1f969', // 🥩
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greengrocer: '1f966', // 🥦
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deli: '1f9c0', // 🧀
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// Shopping
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department_store: '1f3ec', // 🏬
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clothing_store: '1f455', // 👕
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shoe_store: '1f45f', // 👟
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electronics_store: '1f4f1', // 📱
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hardware_store: '1f527', // 🔧
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furniture_store: '1fa91', // 🪑
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bookshop: '1f4d6', // 📖
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newsagent: '1f4f0', // 📰
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charity_shop: '1f49c', // 💜
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shopping_centre: '1f6cd', // 🛍
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optician: '1f453', // 👓
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off_licence: '1f37a', // 🍺
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// Food & Drink
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restaurant: '1f37d', // 🍽
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cafe: '2615', // ☕
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pub: '1f37b', // 🍻
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bar: '1f378', // 🍸
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fast_food: '1f354', // 🍔
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food_court: '1f372', // 🍲
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ice_cream: '1f366', // 🍦
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beer_garden: '1f37a', // 🍺
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// Personal Care
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hairdresser: '1f487', // 💇
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beauty_salon: '1f484', // 💄
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laundry: '1f9fa', // 🧺
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dry_cleaning: '1f455', // 👕
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// Finance
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bank: '1f3e6', // 🏦
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atm: '1f4b3', // 💳
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bureau_de_change: '1f4b1', // 💱
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// Entertainment & Culture
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cinema: '1f3ac', // 🎬
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theatre: '1f3ad', // 🎭
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nightclub: '1f483', // 💃
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community_centre: '1f3db', // 🏛
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arts_centre: '1f3a8', // 🎨
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museum: '1f3db', // 🏛
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gallery: '1f5bc', // 🖼
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attraction: '2b50', // ⭐
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zoo: '1f418', // 🐘
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theme_park: '1f3a2', // 🎢
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viewpoint: '1f301', // 🌁
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// Accommodation
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hotel: '1f3e8', // 🏨
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hostel: '1f6cf', // 🛏
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guest_house: '1f3e1', // 🏡
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campsite: '26fa', // ⛺
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caravan_site: '1f699', // 🚙
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// Religion
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place_of_worship: '1f6d0', // 🛐
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// Government & Public
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town_hall: '1f3db', // 🏛
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courthouse: '2696', // ⚖
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post_office: '1f4ee', // 📮
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prison: '1f513', // 🔓
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public_toilets: '1f6bb', // 🚻
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// Automotive
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petrol_station: '26fd', // ⛽
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ev_charging: '1f50c', // 🔌
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car_dealer: '1f697', // 🚗
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car_repair: '1f527', // 🔧
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parking: '1f17f', // 🅿
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bicycle_parking: '1f6b2', // 🚲
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// Recycling & Waste
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recycling: '267b', // ♻
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waste_disposal: '1f5d1', // 🗑
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};
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function getPOIIconUrl(category: string): string {
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@ -57,29 +142,34 @@ function getPOIIconUrl(category: string): string {
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// Tooltip emojis (these render fine in HTML)
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const TOOLTIP_EMOJIS: Record<string, string> = {
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elementary_school: '🏫',
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school: '🏫',
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high_school: '🎓',
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preschool: '👶',
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college_university: '🎓',
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private_school: '🏫',
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doctor: '👨⚕️',
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dentist: '🦷',
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pharmacy: '💊',
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hospital: '🏥',
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public_health_clinic: '🏥',
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train_station: '🚉',
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bus_station: '🚌',
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metro_station: '🚇',
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light_rail_and_subway_stations: '🚇',
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park: '🌳',
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national_park: '🏞️',
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dog_park: '🐕',
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police_department: '🚔',
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fire_department: '🚒',
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supermarket: '🛒',
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grocery_store: '🛒',
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convenience_store: '🏪',
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school: '🏫', preschool: '👶', college_university: '🎓', library: '📚',
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doctor: '🏥', dentist: '🦷', pharmacy: '💊', hospital: '🏥',
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public_health_clinic: '🏥', veterinary: '🐾', nursing_home: '🏠', social_facility: '🤝',
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train_station: '🚉', bus_station: '🚌', bus_stop: '🚏', metro_station: '🚇',
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light_rail_station: '🚇', tram_stop: '🚊', ferry_terminal: '⛴️', airport: '✈️',
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park: '🌳', national_park: '🏞️', nature_reserve: '🌿', dog_park: '🐕',
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playground: '🎠', garden: '🌺', sports_centre: '🏃', swimming_pool: '🏊',
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gym: '💪', golf_course: '⛳', marina: '⛵',
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police_department: '🚔', fire_department: '🚒',
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supermarket: '🛒', grocery_store: '🛒', convenience_store: '🏪',
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bakery: '🍞', butcher: '🥩', greengrocer: '🥦', deli: '🧀',
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department_store: '🏬', clothing_store: '👕', shoe_store: '👟',
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electronics_store: '📱', hardware_store: '🔧', furniture_store: '🪑',
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bookshop: '📖', newsagent: '📰', charity_shop: '💜', shopping_centre: '🛍️',
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optician: '👓', off_licence: '🍺',
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restaurant: '🍽️', cafe: '☕', pub: '🍻', bar: '🍸',
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fast_food: '🍔', food_court: '🍲', ice_cream: '🍦', beer_garden: '🍺',
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hairdresser: '💇', beauty_salon: '💄', laundry: '🧺', dry_cleaning: '👕',
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bank: '🏦', atm: '💳', bureau_de_change: '💱',
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cinema: '🎬', theatre: '🎭', nightclub: '💃', community_centre: '🏛️',
|
||||
arts_centre: '🎨', museum: '🏛️', gallery: '🖼️', attraction: '⭐',
|
||||
zoo: '🐘', theme_park: '🎢', viewpoint: '🌁',
|
||||
hotel: '🏨', hostel: '🛏️', guest_house: '🏡', campsite: '⛺', caravan_site: '🚙',
|
||||
place_of_worship: '🛐',
|
||||
town_hall: '🏛️', courthouse: '⚖️', post_office: '📮', prison: '🔓', public_toilets: '🚻',
|
||||
petrol_station: '⛽', ev_charging: '🔌', car_dealer: '🚗', car_repair: '🔧',
|
||||
parking: '🅿️', bicycle_parking: '🚲',
|
||||
recycling: '♻️', waste_disposal: '🗑️',
|
||||
};
|
||||
|
||||
function getTooltipEmoji(category: string): string {
|
||||
|
|
@ -158,7 +248,7 @@ function journeyTimeToColor(minutes: number | null | undefined): [number, number
|
|||
}
|
||||
|
||||
function zoomToResolution(zoom: number): number {
|
||||
if (zoom < 8.5) return 7;
|
||||
if (zoom < 7) return 7;
|
||||
if (zoom < 9.5) return 8;
|
||||
if (zoom < 11) return 9;
|
||||
if (zoom < 13) return 10;
|
||||
|
|
@ -209,6 +299,22 @@ interface Dimensions {
|
|||
height: number;
|
||||
}
|
||||
|
||||
// First label layer in the Carto Positron style — hexagons render below this
|
||||
const LABEL_LAYER_ID = 'waterway_label';
|
||||
|
||||
function DeckOverlay({
|
||||
layers,
|
||||
getTooltip,
|
||||
}: {
|
||||
layers: (H3HexagonLayer<HexagonData> | IconLayer<POI>)[];
|
||||
// eslint-disable-next-line @typescript-eslint/no-explicit-any
|
||||
getTooltip: any;
|
||||
}) {
|
||||
const overlay = useControl(() => new MapboxOverlay({ interleaved: true }));
|
||||
overlay.setProps({ layers, getTooltip });
|
||||
return null;
|
||||
}
|
||||
|
||||
export default function Map({ data, pois, onViewChange, colorMode }: MapProps) {
|
||||
const containerRef = useRef<HTMLDivElement>(null);
|
||||
const [viewState, setViewState] = useState<ViewState>(INITIAL_VIEW);
|
||||
|
|
@ -240,12 +346,23 @@ export default function Map({ data, pois, onViewChange, colorMode }: MapProps) {
|
|||
onViewChange({ resolution, bounds, zoom: viewState.zoom });
|
||||
}, [viewState, dimensions, onViewChange]);
|
||||
|
||||
const handleViewStateChange = useCallback((params: { viewState: unknown }) => {
|
||||
const newViewState = params.viewState as ViewState;
|
||||
setViewState(newViewState);
|
||||
const handleMove = useCallback((evt: { viewState: ViewState }) => {
|
||||
setViewState(evt.viewState);
|
||||
}, []);
|
||||
|
||||
// Popup state for POI hover (using screen coordinates)
|
||||
// Make place labels more legible over the colored hexagons
|
||||
const handleMapLoad = useCallback((evt: { target: MapRef['getMap'] extends () => infer M ? M : never }) => {
|
||||
const map = evt.target;
|
||||
for (const layer of map.getStyle().layers || []) {
|
||||
if (layer.type !== 'symbol') continue;
|
||||
// Stronger white halo so text pops over hex fills
|
||||
map.setPaintProperty(layer.id, 'text-halo-color', 'rgba(255,255,255,1)');
|
||||
map.setPaintProperty(layer.id, 'text-halo-width', 2);
|
||||
map.setPaintProperty(layer.id, 'text-color', '#222');
|
||||
}
|
||||
}, []);
|
||||
|
||||
// Popup state for POI hover
|
||||
const [popupInfo, setPopupInfo] = useState<{
|
||||
x: number;
|
||||
y: number;
|
||||
|
|
@ -283,6 +400,9 @@ export default function Map({ data, pois, onViewChange, colorMode }: MapProps) {
|
|||
pickable: true,
|
||||
opacity: 0.5,
|
||||
highPrecision: true,
|
||||
// Render below labels so road names, place names etc. stay visible
|
||||
// @ts-expect-error beforeId is a MapboxOverlay interleave prop, not typed in LayerProps
|
||||
beforeId: LABEL_LAYER_ID,
|
||||
}),
|
||||
new IconLayer<POI>({
|
||||
id: 'poi-icons',
|
||||
|
|
@ -303,7 +423,6 @@ export default function Map({ data, pois, onViewChange, colorMode }: MapProps) {
|
|||
[data, pois, handlePoiHover, colorMode]
|
||||
);
|
||||
|
||||
// Tooltip for hexagons only (POIs use MapLibre popup)
|
||||
const getTooltip = useCallback(({ object }: { object?: HexagonData }) => {
|
||||
if (!object || !('h3' in object)) return null;
|
||||
|
||||
|
|
@ -339,15 +458,15 @@ export default function Map({ data, pois, onViewChange, colorMode }: MapProps) {
|
|||
|
||||
return (
|
||||
<div className="flex-1 h-full relative" ref={containerRef}>
|
||||
<DeckGL
|
||||
viewState={viewState}
|
||||
controller
|
||||
layers={layers}
|
||||
onViewStateChange={handleViewStateChange as never}
|
||||
getTooltip={getTooltip as never}
|
||||
<MapGL
|
||||
{...viewState}
|
||||
onMove={handleMove}
|
||||
onLoad={handleMapLoad as never}
|
||||
mapStyle={MAP_STYLE}
|
||||
style={{ width: '100%', height: '100%' }}
|
||||
>
|
||||
<MapGL mapStyle={MAP_STYLE} />
|
||||
</DeckGL>
|
||||
<DeckOverlay layers={layers} getTooltip={getTooltip as never} />
|
||||
</MapGL>
|
||||
{popupInfo && (
|
||||
<div
|
||||
className="absolute pointer-events-none bg-white rounded shadow-lg p-2 text-sm"
|
||||
|
|
|
|||
|
|
@ -57,13 +57,9 @@ export interface POIResponse {
|
|||
features: POI[];
|
||||
}
|
||||
|
||||
export const POI_CATEGORY_GROUPS = [
|
||||
'schools',
|
||||
'healthcare',
|
||||
'transport',
|
||||
'parks',
|
||||
'emergency',
|
||||
'supermarkets',
|
||||
] as const;
|
||||
export interface POICategoryInfo {
|
||||
emoji: string;
|
||||
label: string;
|
||||
}
|
||||
|
||||
export type POICategoryGroup = (typeof POI_CATEGORY_GROUPS)[number];
|
||||
export type POICategoriesMap = Record<string, POICategoryInfo>;
|
||||
|
|
|
|||
0
pipeline/pois/__init__.py
Normal file
0
pipeline/pois/__init__.py
Normal file
181
pipeline/pois/__main__.py
Normal file
181
pipeline/pois/__main__.py
Normal file
|
|
@ -0,0 +1,181 @@
|
|||
"""Single-pass POI extraction from OSM PBF file using pyosmium."""
|
||||
|
||||
import json
|
||||
import urllib.request
|
||||
|
||||
import osmium
|
||||
import polars as pl
|
||||
from tqdm import tqdm
|
||||
|
||||
from .config import (
|
||||
GB_PBF_FILE,
|
||||
GEOFABRIK_GB_URL,
|
||||
OSM_TAG_MAPPING,
|
||||
OUTPUT_FILE,
|
||||
TAG_KEYS_TO_CHECK,
|
||||
UK_BBOX_EAST,
|
||||
UK_BBOX_NORTH,
|
||||
UK_BBOX_SOUTH,
|
||||
UK_BBOX_WEST,
|
||||
)
|
||||
|
||||
# Approximate element count for the GB PBF extract (for progress estimation).
|
||||
ESTIMATED_ELEMENTS = 500_000_000
|
||||
|
||||
|
||||
def download_pbf() -> None:
|
||||
"""Download Great Britain PBF extract from Geofabrik."""
|
||||
GB_PBF_FILE.parent.mkdir(parents=True, exist_ok=True)
|
||||
tmp = GB_PBF_FILE.with_suffix(".pbf.tmp")
|
||||
print(f"Downloading {GEOFABRIK_GB_URL}")
|
||||
|
||||
with (
|
||||
tqdm(unit="B", unit_scale=True, desc="Downloading") as bar,
|
||||
urllib.request.urlopen(GEOFABRIK_GB_URL) as resp,
|
||||
open(tmp, "wb") as f,
|
||||
):
|
||||
length = resp.headers.get("Content-Length")
|
||||
if length:
|
||||
bar.total = int(length)
|
||||
while chunk := resp.read(1 << 20):
|
||||
f.write(chunk)
|
||||
bar.update(len(chunk))
|
||||
|
||||
tmp.rename(GB_PBF_FILE)
|
||||
print(f"Saved to {GB_PBF_FILE}")
|
||||
|
||||
|
||||
class POIHandler(osmium.SimpleHandler):
|
||||
"""Streams OSM data, filters to UK bbox, extracts matching POIs."""
|
||||
|
||||
def __init__(self, progress: tqdm) -> None:
|
||||
super().__init__()
|
||||
self.pois: list[dict] = []
|
||||
self._poi_count = 0
|
||||
self._progress = progress
|
||||
|
||||
def _in_uk(self, lat: float, lon: float) -> bool:
|
||||
return (
|
||||
UK_BBOX_SOUTH <= lat <= UK_BBOX_NORTH
|
||||
and UK_BBOX_WEST <= lon <= UK_BBOX_EAST
|
||||
)
|
||||
|
||||
def _match_tags(self, tags: osmium.osm.TagList) -> str | None:
|
||||
for key in TAG_KEYS_TO_CHECK:
|
||||
if key in tags:
|
||||
value = tags[key]
|
||||
if value in TAG_KEYS_TO_CHECK[key]:
|
||||
return OSM_TAG_MAPPING[(key, value)]
|
||||
return None
|
||||
|
||||
def _get_name(self, tags: osmium.osm.TagList) -> str:
|
||||
return tags.get("name:en", tags.get("name", ""))
|
||||
|
||||
def _tags_to_json(self, tags: osmium.osm.TagList) -> str:
|
||||
return json.dumps({tag.k: tag.v for tag in tags})
|
||||
|
||||
def _add_poi(
|
||||
self, osm_id: str, tags: osmium.osm.TagList, category: str, lat: float, lng: float
|
||||
) -> None:
|
||||
self.pois.append(
|
||||
{
|
||||
"id": osm_id,
|
||||
"name": self._get_name(tags),
|
||||
"category": category,
|
||||
"lat": lat,
|
||||
"lng": lng,
|
||||
"osm_tags": self._tags_to_json(tags),
|
||||
}
|
||||
)
|
||||
self._poi_count += 1
|
||||
self._progress.set_postfix(pois=f"{self._poi_count:,}", refresh=False)
|
||||
|
||||
def _tick(self) -> None:
|
||||
self._progress.update(1)
|
||||
|
||||
def node(self, n: osmium.osm.Node) -> None:
|
||||
self._tick()
|
||||
if not n.location.valid:
|
||||
return
|
||||
lat, lon = n.location.lat, n.location.lon
|
||||
if not self._in_uk(lat, lon):
|
||||
return
|
||||
category = self._match_tags(n.tags)
|
||||
if category:
|
||||
self._add_poi(f"n{n.id}", n.tags, category, lat, lon)
|
||||
|
||||
def way(self, w: osmium.osm.Way) -> None:
|
||||
self._tick()
|
||||
category = self._match_tags(w.tags)
|
||||
if not category:
|
||||
return
|
||||
|
||||
lats = []
|
||||
lons = []
|
||||
for node in w.nodes:
|
||||
try:
|
||||
lats.append(node.location.lat)
|
||||
lons.append(node.location.lon)
|
||||
except osmium.InvalidLocationError:
|
||||
continue
|
||||
|
||||
if not lats:
|
||||
return
|
||||
|
||||
centroid_lat = sum(lats) / len(lats)
|
||||
centroid_lng = sum(lons) / len(lons)
|
||||
|
||||
if not self._in_uk(centroid_lat, centroid_lng):
|
||||
return
|
||||
|
||||
self._add_poi(f"w{w.id}", w.tags, category, centroid_lat, centroid_lng)
|
||||
|
||||
|
||||
def main() -> None:
|
||||
if not GB_PBF_FILE.exists():
|
||||
download_pbf()
|
||||
|
||||
print(f"=== POI Extraction from {GB_PBF_FILE} ===")
|
||||
print(
|
||||
f"UK bbox: ({UK_BBOX_WEST}, {UK_BBOX_SOUTH}, {UK_BBOX_EAST}, {UK_BBOX_NORTH})"
|
||||
)
|
||||
print(f"Categories: {len(OSM_TAG_MAPPING)}")
|
||||
print()
|
||||
|
||||
with tqdm(
|
||||
total=ESTIMATED_ELEMENTS,
|
||||
unit=" elements",
|
||||
unit_scale=True,
|
||||
desc="Streaming",
|
||||
smoothing=0.05,
|
||||
mininterval=1.0,
|
||||
) as progress:
|
||||
handler = POIHandler(progress)
|
||||
handler.apply_file(str(GB_PBF_FILE), locations=True)
|
||||
|
||||
print(f"Extracted {len(handler.pois):,} POIs")
|
||||
|
||||
if not handler.pois:
|
||||
print("No POIs found.")
|
||||
return
|
||||
|
||||
df = pl.DataFrame(handler.pois)
|
||||
|
||||
OUTPUT_FILE.parent.mkdir(parents=True, exist_ok=True)
|
||||
df.write_parquet(OUTPUT_FILE)
|
||||
print(f"Saved to {OUTPUT_FILE}")
|
||||
|
||||
print("\n=== Summary ===")
|
||||
print(f"Total POIs: {len(df):,}")
|
||||
print("\nPOIs by category:")
|
||||
category_counts = (
|
||||
df.group_by("category")
|
||||
.agg(pl.len().alias("count"))
|
||||
.sort("count", descending=True)
|
||||
)
|
||||
for row in category_counts.iter_rows(named=True):
|
||||
print(f" {row['category']}: {row['count']:,}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
147
pipeline/pois/config.py
Normal file
147
pipeline/pois/config.py
Normal file
|
|
@ -0,0 +1,147 @@
|
|||
"""Configuration for POI extraction from OpenStreetMap."""
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
# File paths
|
||||
DATA_DIR = Path(__file__).parent.parent.parent / "data_sources"
|
||||
GB_PBF_FILE = DATA_DIR / "great-britain-latest.osm.pbf"
|
||||
OUTPUT_FILE = DATA_DIR / "uk_pois.parquet"
|
||||
|
||||
# Geofabrik download URL for Great Britain (~1.1GB PBF, much faster than planet XML)
|
||||
GEOFABRIK_GB_URL = (
|
||||
"https://download.geofabrik.de/europe/great-britain-latest.osm.pbf"
|
||||
)
|
||||
|
||||
# UK bounding box (west, south, east, north) — used for way centroid filtering
|
||||
UK_BBOX_WEST = -7.57
|
||||
UK_BBOX_SOUTH = 49.96
|
||||
UK_BBOX_EAST = 1.68
|
||||
UK_BBOX_NORTH = 58.64
|
||||
|
||||
# OSM tag mapping to categories
|
||||
# Maps (tag_key, tag_value) -> category name
|
||||
OSM_TAG_MAPPING: dict[tuple[str, str], str] = {
|
||||
# Education
|
||||
("amenity", "school"): "school",
|
||||
("amenity", "kindergarten"): "preschool",
|
||||
("amenity", "college"): "college_university",
|
||||
("amenity", "university"): "college_university",
|
||||
("amenity", "library"): "library",
|
||||
("amenity", "language_school"): "school",
|
||||
("amenity", "music_school"): "school",
|
||||
("amenity", "driving_school"): "school",
|
||||
# Healthcare
|
||||
("amenity", "hospital"): "hospital",
|
||||
("amenity", "clinic"): "public_health_clinic",
|
||||
("amenity", "doctors"): "doctor",
|
||||
("amenity", "dentist"): "dentist",
|
||||
("amenity", "pharmacy"): "pharmacy",
|
||||
("amenity", "veterinary"): "veterinary",
|
||||
("amenity", "nursing_home"): "nursing_home",
|
||||
("amenity", "social_facility"): "social_facility",
|
||||
# Transport
|
||||
("railway", "station"): "train_station",
|
||||
("railway", "halt"): "train_station",
|
||||
("railway", "tram_stop"): "tram_stop",
|
||||
("amenity", "bus_station"): "bus_station",
|
||||
("amenity", "ferry_terminal"): "ferry_terminal",
|
||||
("public_transport", "station"): "train_station",
|
||||
("public_transport", "stop_position"): "bus_stop",
|
||||
("station", "subway"): "metro_station",
|
||||
("station", "light_rail"): "light_rail_station",
|
||||
("aeroway", "aerodrome"): "airport",
|
||||
("highway", "bus_stop"): "bus_stop",
|
||||
# Parks & Leisure
|
||||
("leisure", "park"): "park",
|
||||
("leisure", "nature_reserve"): "nature_reserve",
|
||||
("leisure", "dog_park"): "dog_park",
|
||||
("leisure", "playground"): "playground",
|
||||
("leisure", "sports_centre"): "sports_centre",
|
||||
("leisure", "swimming_pool"): "swimming_pool",
|
||||
("leisure", "fitness_centre"): "gym",
|
||||
("leisure", "golf_course"): "golf_course",
|
||||
("leisure", "garden"): "garden",
|
||||
("leisure", "marina"): "marina",
|
||||
("boundary", "national_park"): "national_park",
|
||||
# Emergency
|
||||
("amenity", "police"): "police_department",
|
||||
("amenity", "fire_station"): "fire_department",
|
||||
# Shopping
|
||||
("shop", "supermarket"): "supermarket",
|
||||
("shop", "convenience"): "convenience_store",
|
||||
("shop", "grocery"): "grocery_store",
|
||||
("shop", "bakery"): "bakery",
|
||||
("shop", "butcher"): "butcher",
|
||||
("shop", "greengrocer"): "greengrocer",
|
||||
("shop", "deli"): "deli",
|
||||
("shop", "department_store"): "department_store",
|
||||
("shop", "clothes"): "clothing_store",
|
||||
("shop", "shoes"): "shoe_store",
|
||||
("shop", "electronics"): "electronics_store",
|
||||
("shop", "hardware"): "hardware_store",
|
||||
("shop", "furniture"): "furniture_store",
|
||||
("shop", "car"): "car_dealer",
|
||||
("shop", "car_repair"): "car_repair",
|
||||
("shop", "hairdresser"): "hairdresser",
|
||||
("shop", "beauty"): "beauty_salon",
|
||||
("shop", "optician"): "optician",
|
||||
("shop", "newsagent"): "newsagent",
|
||||
("shop", "books"): "bookshop",
|
||||
("shop", "charity"): "charity_shop",
|
||||
("shop", "alcohol"): "off_licence",
|
||||
("shop", "laundry"): "laundry",
|
||||
("shop", "dry_cleaning"): "dry_cleaning",
|
||||
("shop", "mall"): "shopping_centre",
|
||||
# Food & Drink
|
||||
("amenity", "restaurant"): "restaurant",
|
||||
("amenity", "cafe"): "cafe",
|
||||
("amenity", "pub"): "pub",
|
||||
("amenity", "bar"): "bar",
|
||||
("amenity", "fast_food"): "fast_food",
|
||||
("amenity", "food_court"): "food_court",
|
||||
("amenity", "ice_cream"): "ice_cream",
|
||||
("amenity", "biergarten"): "beer_garden",
|
||||
# Finance
|
||||
("amenity", "bank"): "bank",
|
||||
("amenity", "atm"): "atm",
|
||||
("amenity", "bureau_de_change"): "bureau_de_change",
|
||||
# Entertainment & Culture
|
||||
("amenity", "cinema"): "cinema",
|
||||
("amenity", "theatre"): "theatre",
|
||||
("amenity", "nightclub"): "nightclub",
|
||||
("amenity", "community_centre"): "community_centre",
|
||||
("amenity", "arts_centre"): "arts_centre",
|
||||
("tourism", "museum"): "museum",
|
||||
("tourism", "gallery"): "gallery",
|
||||
("tourism", "attraction"): "attraction",
|
||||
("tourism", "zoo"): "zoo",
|
||||
("tourism", "theme_park"): "theme_park",
|
||||
("tourism", "viewpoint"): "viewpoint",
|
||||
# Accommodation
|
||||
("tourism", "hotel"): "hotel",
|
||||
("tourism", "hostel"): "hostel",
|
||||
("tourism", "guest_house"): "guest_house",
|
||||
("tourism", "camp_site"): "campsite",
|
||||
("tourism", "caravan_site"): "caravan_site",
|
||||
# Religion
|
||||
("amenity", "place_of_worship"): "place_of_worship",
|
||||
# Government & Public
|
||||
("amenity", "townhall"): "town_hall",
|
||||
("amenity", "courthouse"): "courthouse",
|
||||
("amenity", "post_office"): "post_office",
|
||||
("amenity", "prison"): "prison",
|
||||
("amenity", "recycling"): "recycling",
|
||||
("amenity", "waste_disposal"): "waste_disposal",
|
||||
("amenity", "toilets"): "public_toilets",
|
||||
# Fuel
|
||||
("amenity", "fuel"): "petrol_station",
|
||||
("amenity", "charging_station"): "ev_charging",
|
||||
# Parking
|
||||
("amenity", "parking"): "parking",
|
||||
("amenity", "bicycle_parking"): "bicycle_parking",
|
||||
}
|
||||
|
||||
# Build reverse lookup: tag_key -> set of tag_values we care about
|
||||
TAG_KEYS_TO_CHECK: dict[str, set[str]] = {}
|
||||
for (key, value), _ in OSM_TAG_MAPPING.items():
|
||||
TAG_KEYS_TO_CHECK.setdefault(key, set()).add(value)
|
||||
|
|
@ -6,31 +6,47 @@ import polars as pl
|
|||
|
||||
from pipeline.config import AGGREGATES_DIR, H3_RESOLUTIONS, PROCESSED_DIR
|
||||
|
||||
JOURNEY_COLS = [
|
||||
"public_transport_easy_minutes",
|
||||
"public_transport_quick_minutes",
|
||||
"cycling_minutes",
|
||||
]
|
||||
|
||||
AGGREGATE_COLS = [
|
||||
"median_pt_easy_minutes",
|
||||
"median_pt_quick_minutes",
|
||||
"median_cycling_minutes",
|
||||
"median_journey_minutes",
|
||||
]
|
||||
|
||||
|
||||
def aggregate_journey_times(
|
||||
journey_times_path: Path | None = None,
|
||||
postcodes_h3_path: Path | None = None,
|
||||
output_dir: Path | None = None,
|
||||
aggregates_dir: Path | None = None,
|
||||
) -> list[Path]:
|
||||
"""
|
||||
Aggregate journey times by H3 cells at all resolutions.
|
||||
Add journey times to existing H3 aggregate parquet files.
|
||||
|
||||
Joins journey_times_bank.parquet with postcodes_h3.parquet on postcode,
|
||||
then groups by H3 cell to compute median journey time.
|
||||
Joins journey_times_bank_checkpoint.parquet with postcodes_h3.parquet on postcode,
|
||||
aggregates by H3 cell, then merges into existing res{N}.parquet files.
|
||||
"""
|
||||
journey_times_path = journey_times_path or PROCESSED_DIR / "journey_times_bank.parquet"
|
||||
journey_times_path = (
|
||||
journey_times_path
|
||||
or PROCESSED_DIR / "journey_times_bank_checkpoint.parquet"
|
||||
)
|
||||
postcodes_h3_path = postcodes_h3_path or PROCESSED_DIR / "postcodes_h3.parquet"
|
||||
output_dir = output_dir or AGGREGATES_DIR
|
||||
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
aggregates_dir = aggregates_dir or AGGREGATES_DIR
|
||||
|
||||
# Load journey times data
|
||||
journey_df = pl.read_parquet(journey_times_path).select(
|
||||
["postcode", "public_transport_minutes"]
|
||||
["postcode"] + JOURNEY_COLS
|
||||
)
|
||||
|
||||
# Filter out null journey times
|
||||
journey_df = journey_df.filter(pl.col("public_transport_minutes").is_not_null())
|
||||
# Filter out rows where all journey time columns are null
|
||||
journey_df = journey_df.filter(
|
||||
pl.any_horizontal(pl.col(c).is_not_null() for c in JOURNEY_COLS)
|
||||
)
|
||||
|
||||
if journey_df.height == 0:
|
||||
print("No valid journey times found")
|
||||
|
|
@ -48,31 +64,63 @@ def aggregate_journey_times(
|
|||
|
||||
print(f"Joined {joined_df.height} postcodes with journey times")
|
||||
|
||||
saved_paths = []
|
||||
updated_paths = []
|
||||
|
||||
for resolution in H3_RESOLUTIONS:
|
||||
h3_col = f"h3_res{resolution}"
|
||||
parquet_path = aggregates_dir / f"res{resolution}.parquet"
|
||||
|
||||
if not parquet_path.exists():
|
||||
print(f"Skipping resolution {resolution} - {parquet_path} not found")
|
||||
continue
|
||||
|
||||
if h3_col not in joined_df.columns:
|
||||
print(f"Skipping resolution {resolution} - column {h3_col} not found")
|
||||
continue
|
||||
|
||||
# Aggregate by H3 cell - compute median journey time
|
||||
agg_df = (
|
||||
# Aggregate journey times by H3 cell
|
||||
journey_agg = (
|
||||
joined_df.group_by(h3_col)
|
||||
.agg(
|
||||
pl.col("public_transport_minutes").median().alias("median_journey_minutes"),
|
||||
pl.col("public_transport_minutes").count().alias("journey_count"),
|
||||
pl.col("public_transport_easy_minutes")
|
||||
.median()
|
||||
.alias("median_pt_easy_minutes"),
|
||||
pl.col("public_transport_quick_minutes")
|
||||
.median()
|
||||
.alias("median_pt_quick_minutes"),
|
||||
pl.col("cycling_minutes")
|
||||
.median()
|
||||
.alias("median_cycling_minutes"),
|
||||
pl.col("public_transport_quick_minutes")
|
||||
.median()
|
||||
.alias("median_journey_minutes"),
|
||||
)
|
||||
.rename({h3_col: "h3"})
|
||||
)
|
||||
|
||||
output_path = output_dir / f"journey_times_res{resolution}.parquet"
|
||||
agg_df.write_parquet(output_path)
|
||||
saved_paths.append(output_path)
|
||||
print(f"Saved {agg_df.height} cells to {output_path}")
|
||||
# Load existing parquet
|
||||
existing_df = pl.read_parquet(parquet_path)
|
||||
|
||||
return saved_paths
|
||||
# Drop existing journey time columns if present
|
||||
existing_df = existing_df.drop(
|
||||
[c for c in AGGREGATE_COLS if c in existing_df.columns]
|
||||
)
|
||||
|
||||
# Left join journey times onto existing data
|
||||
updated_df = existing_df.join(journey_agg, on="h3", how="left")
|
||||
|
||||
# Save back to parquet
|
||||
updated_df.write_parquet(parquet_path)
|
||||
updated_paths.append(parquet_path)
|
||||
matched = updated_df.filter(
|
||||
pl.col("median_journey_minutes").is_not_null()
|
||||
).height
|
||||
print(
|
||||
f"Updated {parquet_path.name}: {matched} rows with journey times "
|
||||
f"(out of {updated_df.height} total)"
|
||||
)
|
||||
|
||||
return updated_paths
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
|
|||
|
|
@ -5,6 +5,7 @@ import polars as pl
|
|||
from pipeline.sources.postcodes import save_postcodes
|
||||
from pipeline.sources.property_prices import PropertyPricesSource
|
||||
from pipeline.processors.h3_aggregator import save_aggregates
|
||||
from pipeline.processors.journey_times_aggregator import aggregate_journey_times
|
||||
|
||||
|
||||
def run_pipeline():
|
||||
|
|
@ -14,22 +15,31 @@ def run_pipeline():
|
|||
print("=" * 60)
|
||||
|
||||
# Step 1: Process postcodes with H3 indices
|
||||
print("\n[1/3] Processing postcodes with H3 indices...")
|
||||
print("\n[1/4] Processing postcodes with H3 indices...")
|
||||
postcodes_path = save_postcodes()
|
||||
print(f" Saved: {postcodes_path}")
|
||||
|
||||
print("\n[2/3] Processing property prices...")
|
||||
print("\n[2/4] Processing property prices...")
|
||||
postcodes = pl.scan_parquet(postcodes_path)
|
||||
property_source = PropertyPricesSource()
|
||||
properties = property_source.process(postcodes)
|
||||
print(" Joined property prices with postcodes")
|
||||
|
||||
print("\n[3/3] Aggregating at H3 resolutions...")
|
||||
print("\n[3/4] Aggregating at H3 resolutions...")
|
||||
saved_paths = save_aggregates(properties)
|
||||
for path in saved_paths:
|
||||
size_mb = path.stat().st_size / (1024 * 1024)
|
||||
print(f" Saved: {path.name} ({size_mb:.1f} MB)")
|
||||
|
||||
print("\n[4/4] Adding journey times to aggregates...")
|
||||
updated_paths = aggregate_journey_times()
|
||||
if updated_paths:
|
||||
for path in updated_paths:
|
||||
size_mb = path.stat().st_size / (1024 * 1024)
|
||||
print(f" Updated: {path.name} ({size_mb:.1f} MB)")
|
||||
else:
|
||||
print(" Skipped (no journey time data found)")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
run_pipeline()
|
||||
|
|
|
|||
|
|
@ -77,14 +77,28 @@ def query_hexagons_cached(
|
|||
# Filter by year range
|
||||
df = df.filter((pl.col("year") >= min_year) & (pl.col("year") <= max_year))
|
||||
|
||||
# Check which journey time columns exist
|
||||
journey_cols = [
|
||||
"median_journey_minutes",
|
||||
"median_pt_easy_minutes",
|
||||
"median_pt_quick_minutes",
|
||||
"median_cycling_minutes",
|
||||
]
|
||||
available_journey_cols = [c for c in journey_cols if c in df.columns]
|
||||
|
||||
# Aggregate across years (weighted by count)
|
||||
df = df.group_by("h3").agg(
|
||||
agg_exprs = [
|
||||
pl.col("count").sum().alias("count"),
|
||||
(pl.col("avg_price") * pl.col("count")).sum().alias("weighted_price_sum"),
|
||||
pl.col("median_price").median().alias("median_price"),
|
||||
pl.col("min_price").min().alias("min_price"),
|
||||
pl.col("max_price").max().alias("max_price"),
|
||||
)
|
||||
]
|
||||
for jc in available_journey_cols:
|
||||
# Journey time is same across years, just take first non-null
|
||||
agg_exprs.append(pl.col(jc).first())
|
||||
|
||||
df = df.group_by("h3").agg(agg_exprs)
|
||||
|
||||
# Calculate weighted average price
|
||||
df = df.with_columns(
|
||||
|
|
@ -97,8 +111,7 @@ def query_hexagons_cached(
|
|||
)
|
||||
|
||||
# Build response efficiently using Polars
|
||||
df = df.select(
|
||||
[
|
||||
select_cols = [
|
||||
pl.col("h3"),
|
||||
pl.col("count"),
|
||||
pl.col("avg_price").round(2),
|
||||
|
|
@ -106,7 +119,10 @@ def query_hexagons_cached(
|
|||
pl.col("min_price"),
|
||||
pl.col("max_price"),
|
||||
]
|
||||
)
|
||||
for jc in available_journey_cols:
|
||||
select_cols.append(pl.col(jc).round(0))
|
||||
|
||||
df = df.select(select_cols)
|
||||
|
||||
return df.to_dicts()
|
||||
|
||||
|
|
|
|||
|
|
@ -1,9 +1,5 @@
|
|||
"""POI (Points of Interest) API endpoint."""
|
||||
|
||||
import os
|
||||
|
||||
os.environ["POLARS_UNKNOWN_EXTENSION_TYPE_BEHAVIOR"] = "load_as_storage"
|
||||
|
||||
from pathlib import Path
|
||||
|
||||
from fastapi import APIRouter, Query
|
||||
|
|
@ -13,36 +9,190 @@ router = APIRouter()
|
|||
|
||||
DATA_FILE = Path("data_sources/uk_pois.parquet")
|
||||
|
||||
# Categories useful for property buyers
|
||||
POI_CATEGORIES = {
|
||||
"schools": [
|
||||
"elementary_school",
|
||||
"school",
|
||||
"high_school",
|
||||
"preschool",
|
||||
"college_university",
|
||||
"private_school",
|
||||
],
|
||||
"healthcare": [
|
||||
# Category groups with emoji and member categories
|
||||
POI_CATEGORY_GROUPS: dict[str, dict] = {
|
||||
"schools": {
|
||||
"emoji": "🏫",
|
||||
"label": "Schools",
|
||||
"categories": ["school", "preschool", "college_university", "library"],
|
||||
},
|
||||
"healthcare": {
|
||||
"emoji": "🏥",
|
||||
"label": "Healthcare",
|
||||
"categories": [
|
||||
"doctor",
|
||||
"dentist",
|
||||
"pharmacy",
|
||||
"hospital",
|
||||
"public_health_clinic",
|
||||
"veterinary",
|
||||
"nursing_home",
|
||||
"social_facility",
|
||||
],
|
||||
"transport": [
|
||||
},
|
||||
"transport": {
|
||||
"emoji": "🚉",
|
||||
"label": "Transport",
|
||||
"categories": [
|
||||
"train_station",
|
||||
"bus_station",
|
||||
"bus_stop",
|
||||
"metro_station",
|
||||
"light_rail_and_subway_stations",
|
||||
"light_rail_station",
|
||||
"tram_stop",
|
||||
"ferry_terminal",
|
||||
"airport",
|
||||
],
|
||||
"parks": ["park", "national_park", "dog_park"],
|
||||
"emergency": ["police_department", "fire_department"],
|
||||
"supermarkets": ["supermarket", "grocery_store", "convenience_store"],
|
||||
},
|
||||
"parks": {
|
||||
"emoji": "🌳",
|
||||
"label": "Parks & Leisure",
|
||||
"categories": [
|
||||
"park",
|
||||
"national_park",
|
||||
"nature_reserve",
|
||||
"dog_park",
|
||||
"playground",
|
||||
"garden",
|
||||
"sports_centre",
|
||||
"swimming_pool",
|
||||
"gym",
|
||||
"golf_course",
|
||||
"marina",
|
||||
],
|
||||
},
|
||||
"emergency": {
|
||||
"emoji": "🚨",
|
||||
"label": "Emergency",
|
||||
"categories": ["police_department", "fire_department"],
|
||||
},
|
||||
"supermarkets": {
|
||||
"emoji": "🛒",
|
||||
"label": "Supermarkets & Grocery",
|
||||
"categories": [
|
||||
"supermarket",
|
||||
"grocery_store",
|
||||
"convenience_store",
|
||||
"bakery",
|
||||
"butcher",
|
||||
"greengrocer",
|
||||
"deli",
|
||||
],
|
||||
},
|
||||
"shopping": {
|
||||
"emoji": "🛍️",
|
||||
"label": "Shopping",
|
||||
"categories": [
|
||||
"department_store",
|
||||
"clothing_store",
|
||||
"shoe_store",
|
||||
"electronics_store",
|
||||
"hardware_store",
|
||||
"furniture_store",
|
||||
"bookshop",
|
||||
"newsagent",
|
||||
"charity_shop",
|
||||
"shopping_centre",
|
||||
"optician",
|
||||
"off_licence",
|
||||
],
|
||||
},
|
||||
"food_drink": {
|
||||
"emoji": "🍽️",
|
||||
"label": "Food & Drink",
|
||||
"categories": [
|
||||
"restaurant",
|
||||
"cafe",
|
||||
"pub",
|
||||
"bar",
|
||||
"fast_food",
|
||||
"food_court",
|
||||
"ice_cream",
|
||||
"beer_garden",
|
||||
],
|
||||
},
|
||||
"personal_care": {
|
||||
"emoji": "💇",
|
||||
"label": "Personal Care",
|
||||
"categories": [
|
||||
"hairdresser",
|
||||
"beauty_salon",
|
||||
"laundry",
|
||||
"dry_cleaning",
|
||||
],
|
||||
},
|
||||
"finance": {
|
||||
"emoji": "🏦",
|
||||
"label": "Finance",
|
||||
"categories": ["bank", "atm", "bureau_de_change"],
|
||||
},
|
||||
"entertainment": {
|
||||
"emoji": "🎭",
|
||||
"label": "Entertainment & Culture",
|
||||
"categories": [
|
||||
"cinema",
|
||||
"theatre",
|
||||
"nightclub",
|
||||
"community_centre",
|
||||
"arts_centre",
|
||||
"museum",
|
||||
"gallery",
|
||||
"attraction",
|
||||
"zoo",
|
||||
"theme_park",
|
||||
"viewpoint",
|
||||
],
|
||||
},
|
||||
"accommodation": {
|
||||
"emoji": "🏨",
|
||||
"label": "Accommodation",
|
||||
"categories": [
|
||||
"hotel",
|
||||
"hostel",
|
||||
"guest_house",
|
||||
"campsite",
|
||||
"caravan_site",
|
||||
],
|
||||
},
|
||||
"religion": {
|
||||
"emoji": "🛐",
|
||||
"label": "Places of Worship",
|
||||
"categories": ["place_of_worship"],
|
||||
},
|
||||
"government": {
|
||||
"emoji": "🏛️",
|
||||
"label": "Government & Public",
|
||||
"categories": [
|
||||
"town_hall",
|
||||
"courthouse",
|
||||
"post_office",
|
||||
"prison",
|
||||
"public_toilets",
|
||||
],
|
||||
},
|
||||
"automotive": {
|
||||
"emoji": "⛽",
|
||||
"label": "Automotive",
|
||||
"categories": [
|
||||
"petrol_station",
|
||||
"ev_charging",
|
||||
"car_dealer",
|
||||
"car_repair",
|
||||
"parking",
|
||||
"bicycle_parking",
|
||||
],
|
||||
},
|
||||
"recycling": {
|
||||
"emoji": "♻️",
|
||||
"label": "Recycling & Waste",
|
||||
"categories": ["recycling", "waste_disposal"],
|
||||
},
|
||||
}
|
||||
|
||||
# Flatten for quick lookup
|
||||
ALL_CATEGORIES = {cat for cats in POI_CATEGORIES.values() for cat in cats}
|
||||
ALL_CATEGORIES = {
|
||||
cat for group in POI_CATEGORY_GROUPS.values() for cat in group["categories"]
|
||||
}
|
||||
|
||||
# Cache the dataframe
|
||||
_df_cache: pl.DataFrame | None = None
|
||||
|
|
@ -55,14 +205,9 @@ def get_df() -> pl.DataFrame | None:
|
|||
if not DATA_FILE.exists():
|
||||
return None
|
||||
df = pl.read_parquet(DATA_FILE)
|
||||
# Extract fields we need and filter to relevant categories
|
||||
_df_cache = df.select(
|
||||
pl.col("id"),
|
||||
pl.col("names").struct.field("primary").alias("name"),
|
||||
pl.col("categories").struct.field("primary").alias("category"),
|
||||
pl.col("bbox").struct.field("xmin").alias("lng"),
|
||||
pl.col("bbox").struct.field("ymin").alias("lat"),
|
||||
).filter(pl.col("category").is_in(ALL_CATEGORIES))
|
||||
_df_cache = df.select("id", "name", "category", "lat", "lng").filter(
|
||||
pl.col("category").is_in(ALL_CATEGORIES)
|
||||
)
|
||||
return _df_cache
|
||||
|
||||
|
||||
|
|
@ -83,23 +228,20 @@ async def get_pois(
|
|||
if df is None:
|
||||
return {"features": []}
|
||||
|
||||
# Parse bounds
|
||||
try:
|
||||
south, west, north, east = map(float, bounds.split(","))
|
||||
except ValueError:
|
||||
return {"features": []}
|
||||
|
||||
# Get categories to include
|
||||
requested_groups = [g.strip() for g in categories.split(",")]
|
||||
cats_to_include = set()
|
||||
for group in requested_groups:
|
||||
if group in POI_CATEGORIES:
|
||||
cats_to_include.update(POI_CATEGORIES[group])
|
||||
if group in POI_CATEGORY_GROUPS:
|
||||
cats_to_include.update(POI_CATEGORY_GROUPS[group]["categories"])
|
||||
|
||||
if not cats_to_include:
|
||||
return {"features": []}
|
||||
|
||||
# Filter by bounds and categories
|
||||
filtered = df.filter(
|
||||
(pl.col("lat") >= south)
|
||||
& (pl.col("lat") <= north)
|
||||
|
|
@ -108,7 +250,6 @@ async def get_pois(
|
|||
& (pl.col("category").is_in(cats_to_include))
|
||||
)
|
||||
|
||||
# Limit results to avoid overwhelming the frontend
|
||||
MAX_POIS = 5000
|
||||
if len(filtered) > MAX_POIS:
|
||||
filtered = filtered.sample(n=MAX_POIS, seed=42)
|
||||
|
|
@ -118,5 +259,10 @@ async def get_pois(
|
|||
|
||||
@router.get("/poi-categories")
|
||||
async def get_poi_categories() -> dict:
|
||||
"""Get available POI category groups."""
|
||||
return {"categories": list(POI_CATEGORIES.keys())}
|
||||
"""Get available POI category groups with emoji and labels."""
|
||||
return {
|
||||
"categories": {
|
||||
key: {"emoji": group["emoji"], "label": group["label"]}
|
||||
for key, group in POI_CATEGORY_GROUPS.items()
|
||||
}
|
||||
}
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue