dash_delitos_cdmx.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""

@author: Carlos Minutti Martinez <carlos.minutti@iimas.unam.mx>
@author: Miguel Félix Mata Rivera <mmatar@ipn.mx>


Resumen:
    
    El sistema web desarrollado tiene como objetivo modelar el riesgo espacio-temporal de diferentes tipos de delitos en la Ciudad de México mediante el uso de datos abiertos. Para ello, se utiliza un enfoque basado en mezclas gaussianas, que permite estimar el riesgo relativo de ocurrencia de un delito en un lugar determinado, para diferentes periodos de tiempo específicos.
La exposición a la violencia y el delito puede causar estrés, ansiedad, depresión y otros problemas de salud mental, así como lesiones físicas. Además, la inseguridad puede limitar el acceso a servicios de atención médica y la capacidad de realizar actividades al aire libre, lo que puede afectar la salud de las personas.

La modelización del riesgo de delitos es un aspecto importante en la prevención y control del crimen, y la técnica de mezclas gaussianas ha demostrado ser útil para analizar patrones complejos de datos espaciales y temporales. En este sentido, el sistema web permite a los usuarios visualizar mapas de riesgos, para ser utilizados en posibles análisis de tendencias y comparaciones entre diferentes tipos de delitos y áreas geográficas.

Datos abiertos:
    Carpetas de investigación PGJ
    https://datos.cdmx.gob.mx/dataset/carpetas-de-investigacion-pgj-cdmx
    

Licencia:    
                                 Apache License
                           Version 2.0, January 2004
                        http://www.apache.org/licenses/

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"""

import pandas as pd
import numpy as np
import plotly.express as px 
import plotly.graph_objects as go
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output

from plotly.subplots import make_subplots

import sqlite3
import matplotlib.pyplot as plt 

from sklearn.mixture import GaussianMixture
from mpl_toolkits.axes_grid1 import make_axes_locatable


class CFG:
    base_dir = './'
    seed = 0
    n_clust = 15
    max_pts = 100



BBox = (-99.3394626438541, -98.9488773, 19.1357929998345, 19.5766097622267)    


delitos_l = ["TODOS",
             "ROBO DE VEHÍCULO CON Y SIN VIOLENCIA", 
            "ROBO A TRANSEUNTE EN VÍA PÚBLICA CON Y SIN VIOLENCIA", 
            "ROBO A NEGOCIO CON VIOLENCIA", 
            "ROBO A REPARTIDOR CON Y SIN VIOLENCIA", 
            "LESIONES DOLOSAS POR DISPARO DE ARMA DE FUEGO", 
            "ROBO A PASAJERO A BORDO DEL METRO CON Y SIN VIOLENCIA", 
            "HOMICIDIO DOLOSO", 
            "ROBO A PASAJERO A BORDO DE MICROBUS CON Y SIN VIOLENCIA", 
            "VIOLACIÓN", 
            "ROBO A CASA HABITACIÓN CON VIOLENCIA", 
            "ROBO A CUENTAHABIENTE SALIENDO DEL CAJERO CON VIOLENCIA", 
            "ROBO A PASAJERO A BORDO DE TAXI CON VIOLENCIA", 
            "ROBO A TRANSPORTISTA CON Y SIN VIOLENCIA", 
            "SECUESTRO"]

def map_plot(pars):
    print(pars)
    
    a1 = pars['anio1']
    a2 = pars['anio2']
    n_clust = pars['n_clust']
    d = pars['delitos']
    d_sql = ''
    
    for (i,x) in enumerate(d):
        d_sql += f'categoria_delito="{x}"'
        if i<(len(d)-1):
            d_sql += ' OR '
            
        
        
    con = sqlite3.connect(CFG.base_dir + 'delitos.sqlite')
    cur = con.cursor()

    delitos = pd.read_sql_query(f'SELECT * FROM pgj WHERE ({d_sql}) AND ao_inicio>={a1} AND ao_inicio<={a2}', con)    
    con.close()
    print(f'SELECT * FROM pgj WHERE ({d_sql}) AND ao_inicio>={a1} AND ao_inicio<={a2}')
    print(delitos)
    
    
    X = delitos[['longitud', 'latitud']]
    idx_na = X.isna().transpose().any()
    
    delitos = delitos[~idx_na]
    X = X[~idx_na]
    
    
    np.random.seed(CFG.seed)
    gm = GaussianMixture(n_components=n_clust, random_state=CFG.seed, n_init=10, init_params='kmeans').fit(X)

    
    rnd = np.random.rand(X.shape[0])
    tmp = rnd.copy()
    tmp.sort()
    samp =  rnd<tmp[CFG.max_pts]
    
    
    x = np.linspace(BBox[0],BBox[1], 50)
    y = np.linspace(BBox[2],BBox[3], 60)
    X_m, Y_m = np.meshgrid(x, y)
    G = pd.DataFrame([X_m.flatten(), Y_m.flatten()]).transpose()
    Z_m = np.exp(gm.score_samples(G))
    Z_m = Z_m.reshape([60,50])
    


    import base64

    map_png = base64.b64encode(open('cdmx2.png', 'rb').read())
    
    fig = go.Figure()
    

    fig.add_trace(
        go.Contour(z=Z_m, x=x, y=y, contours_coloring='lines',
        line_width=2)
    )
    

    fig.add_layout_image(
            dict(
                source='data:image/png;base64,{}'.format(map_png.decode()),
                #source='data:image/svg+xml;base64,{}'.format(map_png.decode()),
                xref="x", yref="y",
                    x=BBox[0], y=BBox[3],
                    sizex=BBox[1]-BBox[0], sizey=BBox[3]-BBox[2],
                    xanchor="left",
                    yanchor="top",
                    #sizing="stretch",
                    layer="below"))
    

    fig.update_layout(template="plotly_white")    


    fig.update_layout(width=int(1000*(BBox[1]-BBox[0])/(BBox[3]-BBox[2])))
    fig.update_layout(height=1000, margin={"r":0,"t":0,"l":0,"b":0})

    return fig



app = dash.Dash(__name__)


edad_list = range(121)
sexo_list = ['M', 'F']
talla_list = range(20, 251)
peso_list = range(201)

app.layout = html.Div([
    html.H1("Delitos en CDMX", style={'text-align': 'center', 'color': '#666666'}),
    
    html.Div("Desde el año:", style={'color': '#333333', 'width':'150px', 'float':'left', 'margin-left':'20px'}),
    html.Div("Hasta el año:", style={'color': '#333333', 'width':'150px', 'float':'left', 'margin-left':'10px'}),
    html.Div("Número de clusters:", style={'color': '#333333', 'width':'150px', 'float':'left', 'margin-left':'10px'}),
    html.Div("Delito:", style={'color': '#333333', 'width':'150px', 'float':'left', 'margin-left':'10px'}),
    html.Br(),

    dcc.Dropdown(
        id='year1-select', 
        options=[{'label': i, 'value': i} for i in range(2016,2020)],
        value=2016,
        style={'width':'150px','float':'left', 'margin-left':'10px'}
    ),
    
    dcc.Dropdown(
        id='year2-select', 
        options=[{'label': i, 'value': i} for i in range(2016,2020)],
        value=2019, 
        style={'width':'150px','float':'left', 'margin-left':'10px'}
    ),

    dcc.Dropdown(
        id='n_clust-select', 
        options=[{'label': i, 'value': i} for i in range(2,21)],
        value=15, 
        style={'width':'150px','float':'left', 'margin-left':'10px'}
    ),

    

    dcc.Dropdown(
        id='delitos-list',
        options=[{'label': f"{i}", 'value': i} for i in delitos_l],
        value=[delitos_l[0]], 
        multi=True,
        searchable=True,
        style={'width':'670px','float':'left', 'margin-left':'10px'}
    ),

    html.Br(),    
    html.P(' ', style={'height':'20px'}),
        
    dcc.Graph(id='delitos-map', figure={})
])




@app.callback(
    Output('delitos-map', 'figure'),
    [Input('year1-select', 'value'),
    Input('year2-select', 'value'),
    Input('n_clust-select', 'value'),
    Input('delitos-list', 'value')]
)
def update_graph_p(anio1, anio2, n_clust, d_lst):

    pars = {}
    pars['anio1'] = anio1
    pars['anio2'] = anio2
    pars['n_clust'] = n_clust

    if pd.Series(delitos_l[0]).isin(d_lst)[0]:
        pars['delitos'] = delitos_l[1:]
    else:
        pars['delitos'] = d_lst

    
    return map_plot(pars)



# ------------------------------------------------------------------------------
if __name__ == '__main__':
    app.run_server(host='0.0.0.0', debug=False)