Fabion Kauker fhk

First Steps for a Citywide Fiber Network

Here's a breakdown of the initial steps you'd take to initiate a citywide fiber project, incorporating elements from the provided feasibility studies and focusing on funding, ISP evaluation, and infrastructure considerations:

Phase 1: Strategy & Policy Development

Establish a Broadband Policy: Define the city's vision for broadband. This should cover:

Goals: Ubiquity, affordability, competition, open access, desired speeds, and financial sustainability.
Roles: The city's role as owner, facilitator, or partner.
Community Engagement: Processes for gathering public input and ongoing communication.


	// DesignCanvas.jsx — Figma-ish design canvas wrapper
	// Warm gray grid bg + Sections + Artboards + PostIt notes.
	// Artboards are reorderable (grip-drag), labels/titles are inline-editable,
	// and any artboard can be opened in a fullscreen focus overlay (←/→/Esc).
	// State persists to a .design-canvas.state.json sidecar via the host
	// bridge. No assets, no deps.
	//
	// Usage:
	// <DesignCanvas>

	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>Fiber Construction Cost Calculator</title>
	<script src="https://cdn.tailwindcss.com"></script>
	<link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap" rel="stylesheet">
	<style>
	body {

	import momepy
	import geopandas as gpd

	def create_graph(linestrings):
	gdf = gpd.GeoDataFrame(geometry=linestrings)
	graph = momepy.gdf_to_nx(gdf, approach="primal")
	return graph

	from shapely.geometry import LineString, Point
	from shapely.ops import unary_union
	from geopy.distance import geodesic

	def snap_to_nearest_node(point, nodes, tolerance):
	for node in nodes:
	if geodesic((point.y, point.x), (node.y, node.x)).meters <= tolerance:
	return node
	return point

	# Some quick snippets for points to lines

	address_df = pd.DataFrame([[0, Point([c for c in h3.cell_to_latlng(h3_8)[::-1]])]], columns=['id', 'geometry'])
	address_gdf = gpd.GeoDataFrame(address_df, geometry="geometry", crs="epsg:4326")

	gdf = gpd.read_file(
	"./data/all_the_data.fgb",
	engine="pyogrio", bbox=location_box)
	gdf = gdf.to_crs('epsg:3310') # https://epsg.io/3310

	import sys
	import json
	import csv


	def main():
	input_json = sys.argv[1]
	output_csv_data = [['name', 'lastdate', 'desc', 'shorturl']]
	with open(input_json, mode='r') as ijson:
	data = json.load(ijson)

	#!/Usr/bin/env python
	# -- coding: utf-8 --

	'''
	Searches a graph and yields all the minimum spanning trees in order of increasing cost.
	This could be used to solve minimum spanning trees with constraints by yielding trees until
	we reach the first one which satisfies a constraint.
	For example it could solve the degree constrained minimum spanning tree DCMST
	'''

	wget https://golang.org/dl/go1.16.7.linux-amd64.tar.gz;
	rm -rf /usr/local/go && tar -C /usr/local -xzf go1.16.7.linux-amd64.tar.gz;
	export PATH=$PATH:/usr/local/go/bin;
	export GOPATH=$HOME/go;
	export PATH=$PATH:$GOROOT/bin:$GOPATH/bin;
	go get github.com/fraugster/parquet-go/cmd/csv2parquet;