Scripts que fazem o processamento de dados do input do usuário

noepicentro, updated 🕥 2022-12-08 10:49:00

Back end

Esse repositório contém os scripts em Python que alimentam o aplicativo.

Na prática, assim que o usuário informa sua localização, a API do site chama os métodos que estão no diretório code para obter os dados necessários para a visualização de dados.

Os dados usados vêm do Censo de 2010 IBGE e do Brasil.io.

Limitações

É importante frisar que o raio populacional exibido é referente ao total de habitantes da região do usuário na época de realização do último Censo, há cerca de dez anos. Esses são os dados mais recentes possíveis para esse nível de detalhamento.

Além disso, o cálculo parte do pressuposto que a população é distribuída homogeneamente dentro de cada setor censitário, o que não é necessariamente verdade, especialmente para blocos de maior área.

Replicação passo-a-passo

Para reproduzir os resultados em sua própria máquina, você precisa configurar seu ambiente de desenvolvimento da seguinte maneira:

  1. Clone o repositório

  2. Baixe este arquivo compactado e extraia no diretório data.

  3. Instale os requerimentos em um ambiente virtual do Anaconda com os seguinte comandos:

conda create -n gpd_0.8 conda activate gpd_0.8 conda config --env --add channels conda-forge conda config --env --set channel_priority strict conda install python=3 geopandas conda install pygeos --channel conda-forge conda install feather-format conda install requests

Ou, caso prefira isntalar com outro método, veja a sessão Using the optional PyGEOS dependency da documentação do GeoPandas.

  1. Caso queira gerar também os pontos que aparecem no mapa, execute python generate_points.py. Isso deve demorar um bocado e é opcional.

  2. Use python prepare.py para pré-processar diversos dados, o que vai otimizar o processo de cálculo.

  3. Use python run_query.py lat lon para obter um objeto JSON-like com as informações necessárias para gerar a visualização de dados personalizada.

  4. O arquivo update.py deve ser executado repetidamente em um intervalo fixo de tempo via cron ou mecanismo semelhante. Ele é responsável por atualizar a contagem de casos de covid-19 no país, além de já calcular previamente o raio de mortes nas principais cidades do país.

Tenha em mente que o aplicativo roda em um ambiente Anaconda, criado exatamente seguindo as especificações desse tutorial, em um servidor Ubuntu 18.04. Não fizemos testes em outras configurações, mas sinta-se a vontade para abrir um issue caso encontre algum problema.

Estrutura do repositório

Diretório code

Esse diretório contém os scripts que processam os dados necessários para o funcionamento.

  • app.py: implementa a API do aplicativo usando o framework Flask

  • generate_points.py: gera um arquivo no formato geojson com (aproximadamente) um ponto para cada habitante do país. O cálculo é feito gerando pontos de forma aleatória em um bounding box que envolve cada setor censitário. Ao fim do processo, removemos os pontos que foram gerados fora do setor. Para minimzar estes erros, estimamos a razão entre as áreas do bounding box e a área do setor e geramos pontos suficientes para compensar aqueles que ficaram fora dos limites. Usamos essa estratégia porque gerar apenas pontos que estejam garantidamente dentro do setor censitário, um polígono complexo, é computacionalmente dispendioso.

  • prepare.py: script que encapsula as funções abaixo, que servem para pré-processar os dados necessários para execução dos cálculos

  • prepare_capitals_radius.py: calcula o raio de mortes a partir de locais turísticos de dez capitais brasileiras e salva em um arquivo JSON. O método de cálculo é o mesmo utilizado no arquivo run_query.py.

  • prepare_city_bboxes.py: divide o mapa do Brasil em 400 bounding boxes que contém os limites dos municípios e salva o resultado em arquivos no formato feather. Esse pré-processamento é necessário para melhorar o tempo de execução da função do arquivo run_query.py que encontra a cidade mais próxima ao usuário.

  • prepate_city_centroids.py: salva os centróides das cidades do país em um arquivo no formato feather, otimizado para melhorar o tempo de leitura.

  • prepare_covid_count.py: envia uma requisição para os servidores do Brasil.io e processa a resposta, salvando um arquivo JSON com dados sobre a quantidade de mortes por Covid 19 no país.

  • prepare_tracts_bboxes.py: Usa o mesmo método do arquivo prepare_city_bboxes.py para dividir os setores censitários do Brasil em cerca de 10 mil arquivos diferentes, otimizando o tempo de carregamento.

  • run_query.py: arquivo que gera os dados que são exibidos para o usuário do aplicativo: o raio de mortes ao redor da localização, a cidade mais próxima que desapareceria e o raio de mortes ao redor do centro de duas capitais. Descrevemos em mais detalhes como a computação funciona na sessão Metodologia detalhada, logo abaixo.

  • update.py: script que encapsula as funções de e prepare_capitals_radius.py e prepare_covid_count.py, de forma a atualizar periodicamente os dados estáticos do alicativo.

Diretório data

Contém informações sobre a divisão do país em setores censitários. Esses arquivos serão pré-processados para permitir que a execução aconteça de forma mais rápida.

Metodologia detalhada

O algoritmo que calcula as informações exibidas para o usuário foi implementado no arquivo run_query.py. Confira abaixo uma descrição passo-a-passo do processo:

  1. Ao receber o input do usuário, o script transforma as coordenadas em objeto Point do Shapely.

point = parse_input(point)

  1. O programa acessa o arquivo JSON gerado por prepare_covid_count.py para descobrir o total de pessoas que precisam estar contidas no raio.

target = get_covid_count(measure='deaths')

  1. O arquivo feather que contém os setores censitários ao redor da localização do usuário é carregado na memória. Caso necessário, áreas adjacentes vão sendo adicionadas até que os setores censitários selecionados tenham uma população superior ao total de mortos por Covid-19 no Brasil.

gdf = find_user_area(point, target)

  1. Alguns setores censitários são polígonos inválidos que se auto-intercepam. Para corrigir esse problema, um buffer com distância 0 é aplicado nas geometrias, como especificado no manual do Shapely.

gdf["geometry"] = gdf.geometry.buffer(0)

  1. Antes de passar para o cáculo do raio, o programa cria um spatial index para otimizar as operações geoespaciais.

spatial_index = gdf.sindex

  1. Aqui ocorre a parte mais densa do processamento, quando o programa computa o raio ao redor do usuário.

radius_data = find_radius(point, gdf, spatial_index, target)

Essa função, por ser a mais complexa do programa, merece uma descrição mais detalhada.

a) De início, o ponto fornecido pelo usuário é colocado em cima do mapa.

b) Um buffer de 0.01 graus é aplicado no ponto, que assim se torna um polígono circular.

c) Verificamos então quais setores censitários fazem interseção com o círculo.

d) Calulamos o percentual de interseção de cada um desses setores e somamos a população de forma proporcional. Por exemplo, caso um setor censitário de 100 habitantes esteja completamente dentro do círculo, somamos as 100 pessoas que moram lá. Caso esse mesmo setor só esteja 30% dentro do círculo, somamos apenas 30 pessoas.

e) Caso a população no raio seja inferior ao total de mortos por Covid-19 no Brasil, aumentamos o buffer em 50% e repetimos o processo a partir do passo C.

f) Definimos um intervalo de tolerância para a população que está dentro do círculo: entre 90% e 110% do total de mortos.

g) Definimos um degrau para alterar o tamanho do buffer a cada iteração subsequente: de início, 50%.

h) Caso o total de pessoas no círculo esteja dentro do intervalo de tolerância, a função se resolve e retorna as coordenadas do raio.

i) Caso o total de pessoas no círculo seja superior ao intervalo de tolerância, diminuímos o tamanho do raio pelo valor do degrau.

j) Caso o total de pessoas no círculo seja inferior ao intervalo de tolerância, aumentamos o tamanho do raio pelo valor do degrau.

k) Verificamos novamente quais setores censitários fazem interseção com o círculo.

l) Calulamos outra vez o percentual de interseção de cada um desses setores e somamos a população de forma proporcional.

m) Para as próximas iterações, o degrau é diminuído pela metade: em vez de 50%, a alteração de tamanho passa para 25%, 12.5%... e assim sucessivamente.

n) Se necessário, repetimos a operação a partir do item H.

  1. O programa acessa a malha de municípios do Brasil para descobrir qual deles contém o ponto do usuário. Essas informações são salvas e retornadas ao fim da execução.

city_data = find_user_city(point, target)

  1. É carregado o arquivo com os centróides dos municípios, que vai ser usado para calcular quais são as cidades mais próximas do usuário.

city_centroids = gpd.read_feather("../output/city_centroids.feather")

  1. Usando a função nearest_neighbor do Shapely, o programa calcula qual é a cidade mais próxima do usuário que iria "desaparecer" - ou seja, que tem menos habitantes do que o total de mortes no Brasil

neighbor_data = find_neighboring_city(point, target, city_centroids)

  1. Para destacar os efeitos da epidemia em centros urbanos grandes, o programa seleciona duas capitais: a primeira é a mais perto do usuário, escolhida usando o mesmo método do item 9. A segunda é escolhida de forma aleatória.

capitals_data = choose_capitals(point, city_data["code_muni"], city_centroids)

  1. O programa devolve para o front-end um objeto JSON com todos os dados coletados.

Issues

Bump certifi from 2020.4.5.2 to 2022.12.7

opened on 2022-12-08 10:48:59 by dependabot[bot]

Bumps certifi from 2020.4.5.2 to 2022.12.7.

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Bump nbconvert from 5.6.1 to 6.5.1

opened on 2022-08-23 18:16:33 by dependabot[bot]

Bumps nbconvert from 5.6.1 to 6.5.1.

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Sourced from nbconvert's releases.

Release 6.5.1

No release notes provided.

6.5.0

What's Changed

New Contributors

Full Changelog: https://github.com/jupyter/nbconvert/compare/6.4.5...6.5

6.4.3

What's Changed

New Contributors

Full Changelog: https://github.com/jupyter/nbconvert/compare/6.4.2...6.4.3

6.4.0

What's Changed

New Contributors

... (truncated)

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Bump mistune from 0.8.4 to 2.0.3

opened on 2022-07-29 23:14:57 by dependabot[bot]

Bumps mistune from 0.8.4 to 2.0.3.

Release notes

Sourced from mistune's releases.

Version 2.0.2

Fix escape_url via lepture/mistune#295

Version 2.0.1

Fix XSS for image link syntax.

Version 2.0.0

First release of Mistune v2.

Version 2.0.0 RC1

In this release, we have a Security Fix for harmful links.

Version 2.0.0 Alpha 1

This is the first release of v2. An alpha version for users to have a preview of the new mistune.

Changelog

Sourced from mistune's changelog.

Changelog

Here is the full history of mistune v2.

Version 2.0.4


Released on Jul 15, 2022

    

  • Fix url plugin in <a> tag
    • 

  • Fix * formatting
    • 



Version 2.0.3

Released on Jun 27, 2022

  • Fix table plugin
  • Security fix for CVE-2022-34749

Version 2.0.2


Released on Jan 14, 2022

Fix escape_url


Version 2.0.1

Released on Dec 30, 2021

XSS fix for image link syntax.

Version 2.0.0


Released on Dec 5, 2021

This is the first non-alpha release of mistune v2.


Version 2.0.0rc1

Released on Feb 16, 2021

Version 2.0.0a6


</tr></table>

... (truncated)

Commits
  • 3f422f1 Version bump 2.0.3
  • a6d4321 Fix asteris emphasis regex CVE-2022-34749
  • 5638e46 Merge pull request #307 from jieter/patch-1
  • 0eba471 Fix typo in guide.rst
  • 61e9337 Fix table plugin
  • 76dec68 Add documentation for renderer heading when TOC enabled
  • 799cd11 Version bump 2.0.2
  • babb0cf Merge pull request #295 from dairiki/bug.escape_url
  • fc2cd53 Make mistune.util.escape_url less aggressive
  • 3e8d352 Version bump 2.0.1
  • Additional commits viewable in compare view


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Bump notebook from 6.0.3 to 6.4.12

opened on 2022-06-16 23:50:53 by dependabot[bot]

Bumps notebook from 6.0.3 to 6.4.12.

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Bump ipython from 7.13.0 to 7.16.3

opened on 2022-01-21 20:32:41 by dependabot[bot]

Bumps ipython from 7.13.0 to 7.16.3.

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Bump pygments from 2.6.1 to 2.7.4

opened on 2021-03-30 00:48:33 by dependabot[bot]

Bumps pygments from 2.6.1 to 2.7.4.

Release notes

Sourced from pygments's releases.

2.7.4

  • Updated lexers:

    • Apache configurations: Improve handling of malformed tags (#1656)

    • CSS: Add support for variables (#1633, #1666)

    • Crystal (#1650, #1670)

    • Coq (#1648)

    • Fortran: Add missing keywords (#1635, #1665)

    • Ini (#1624)

    • JavaScript and variants (#1647 -- missing regex flags, #1651)

    • Markdown (#1623, #1617)

    • Shell

      • Lex trailing whitespace as part of the prompt (#1645)
      • Add missing in keyword (#1652)

    • SQL - Fix keywords (#1668)

    • Typescript: Fix incorrect punctuation handling (#1510, #1511)

  • Fix infinite loop in SML lexer (#1625)

  • Fix backtracking string regexes in JavaScript/TypeScript, Modula2 and many other lexers (#1637)

  • Limit recursion with nesting Ruby heredocs (#1638)

  • Fix a few inefficient regexes for guessing lexers

  • Fix the raw token lexer handling of Unicode (#1616)

  • Revert a private API change in the HTML formatter (#1655) -- please note that private APIs remain subject to change!

  • Fix several exponential/cubic-complexity regexes found by Ben Caller/Doyensec (#1675)

  • Fix incorrect MATLAB example (#1582)

Thanks to Google's OSS-Fuzz project for finding many of these bugs.

2.7.3

... (truncated)

Changelog

Sourced from pygments's changelog.

Version 2.7.4

(released January 12, 2021)

  • Updated lexers:

    • Apache configurations: Improve handling of malformed tags (#1656)

    • CSS: Add support for variables (#1633, #1666)

    • Crystal (#1650, #1670)

    • Coq (#1648)

    • Fortran: Add missing keywords (#1635, #1665)

    • Ini (#1624)

    • JavaScript and variants (#1647 -- missing regex flags, #1651)

    • Markdown (#1623, #1617)

    • Shell

      • Lex trailing whitespace as part of the prompt (#1645)
      • Add missing in keyword (#1652)

    • SQL - Fix keywords (#1668)

    • Typescript: Fix incorrect punctuation handling (#1510, #1511)

  • Fix infinite loop in SML lexer (#1625)

  • Fix backtracking string regexes in JavaScript/TypeScript, Modula2 and many other lexers (#1637)

  • Limit recursion with nesting Ruby heredocs (#1638)

  • Fix a few inefficient regexes for guessing lexers

  • Fix the raw token lexer handling of Unicode (#1616)

  • Revert a private API change in the HTML formatter (#1655) -- please note that private APIs remain subject to change!

  • Fix several exponential/cubic-complexity regexes found by Ben Caller/Doyensec (#1675)

  • Fix incorrect MATLAB example (#1582)

Thanks to Google's OSS-Fuzz project for finding many of these bugs.

Version 2.7.3

(released December 6, 2020)

... (truncated)

Commits
  • 4d555d0 Bump version to 2.7.4.
  • fc3b05d Update CHANGES.
  • ad21935 Revert "Added dracula theme style (#1636)"
  • e411506 Prepare for 2.7.4 release.
  • 275e34d doc: remove Perl 6 ref
  • 2e7e8c4 Fix several exponential/cubic complexity regexes found by Ben Caller/Doyensec
  • eb39c43 xquery: fix pop from empty stack
  • 2738778 fix coding style in test_analyzer_lexer
  • 02e0f09 Added 'ERROR STOP' to fortran.py keywords. (#1665)
  • c83fe48 support added for css variables (#1633)
  • Additional commits viewable in compare view


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