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mehak-sachdeva / README.md
Last active May 17, 2017 20:10
NYC Data Science Academy Workshop <05.17.2017>
@mehak-sachdeva
mehak-sachdeva / README.md
Last active April 28, 2017 20:31
CARTO-Camp <04.28.2017>

Moran's I - Usecases

Public Health:

  • The branch of Epidemiology, a cornerstone of public health, is based on the practice of identifying risk factors for disease and shapes policy decisions finding patterns and abnormalities in the evidence.
  • Among other factors, spatial variations in disease incidences and health outcomes can play a crucial role in evaluating policies around health-care distribution and performance. Analysis of visible spatial patterns in data, using Moran's I, can lead to evidence of patterns of dependence and the level of noise in the data. Moran's I study provides with clustering patterns and concentrated abnormalities that allow the investigator to study to illuminate any unusual patterns and explore reasons for variations beyond normal in those areas. Detection of such patterns can be a crucial first step toward recognizing emerging environmental hazards or even persistent errors in data recording methodology.

Network and Transportation:

  • To accurately detect in
@mehak-sachdeva
mehak-sachdeva / Chart.js
Last active September 23, 2021 13:36
Interactive Charts
/*!
* Chart.js
* http://chartjs.org/
* Version: 2.3.0
*
* Copyright 2016 Nick Downie
* Released under the MIT license
* https://github.com/chartjs/Chart.js/blob/master/LICENSE.md
*/
(function(f){if(typeof exports==="object"&&typeof module!=="undefined"){module.exports=f()}else if(typeof define==="function"&&define.amd){define([],f)}else{var g;if(typeof window!=="undefined"){g=window}else if(typeof global!=="undefined"){g=global}else if(typeof self!=="undefined"){g=self}else{g=this}g.Chart = f()}})(function(){var define,module,exports;return (function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s})({1:[function(require,modul

##URL for the gist : http://bit.ly/2dxJxUM

##Data Observatory: We will query the real-estate data for Los Angeles, CA. We start with an empty map and rename the table to LA:

INSERT INTO LA(the_geom, name)
SELECT *
FROM OBS_GetBoundariesByPointAndRadius(