File:Kyllikki saari murder site ringed negative exponential 1 1 1 1.png
![File:Kyllikki saari murder site ringed negative exponential 1 1 1 1.png](https://upload.wikimedia.org/wikipedia/commons/thumb/a/a6/Kyllikki_saari_murder_site_ringed_negative_exponential_1_1_1_1.png/276px-Kyllikki_saari_murder_site_ringed_negative_exponential_1_1_1_1.png?20230830134249)
Original file (341 × 741 pixels, file size: 116 KB, MIME type: image/png)
Captions
Captions
Summary
[edit]DescriptionKyllikki saari murder site ringed negative exponential 1 1 1 1.png |
English: Murder site of Kyllikki Saari - ringed negative exponential analysis. Bike and grave locations. |
Date | |
Source | Own work |
Author | Merikanto |
This image uses OpenStreetMap
Open Database Licence
https://www.openstreetmap.org/copyright
Sites data from Finnish Wikipedia article.
Python 3 code
- crime location analysis by
- truncated negative exponential
- python3 source code, uses folium
-
- test program only, maybe buggy
- here murder on Kyllikki Saari, Isojoki, Finland
- python 3 script
-
- 30.8.2023 v. 0000.0000.0002
import os
import time
import math
from math import pow
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import folium
from folium import plugins
from selenium import webdriver
import branca
from scipy.interpolate import griddata
import geojsoncontour
import scipy as sp
import scipy.ndimage
import scipy.stats as st
- normal1=1/np.sqrt(2*np.pi)*np.exp(-np.power(x,2)/2)
dimx=500
dimy=500
kenterlon1=21.951671
kenterlat1=62.144996
- olon1=21.92
- olat1=62.125
- olon2=21.98
- olat2=62.155
olon1=21.88
olat1=62.08
olon2=22.00
olat2=62.20
- param_f = 1
- param_g = 2
- dist_coeff=10
- for truncatednegative exponnetial distr
param_f = 1 ## inverse outer decay rate
param_g = 2 ## inverse inner decay rate
dist_coeff=1
sw1=[ olat1, olon1]
ne1=[ olat2, olon2]
rosmo1=np.zeros((dimy, dimx))
rosmo1=rosmo1*1
data =[[ 62.144996, 21.951671, 1. ],
[ 62.133412, 21.956548, 1. ]]
- [ 62.1406, 21.9481, 1. ]]
- minimum radius oh highest propab
- calculatend in below
- buffer_dist = 0
data2=np.array(data)
dimlon=olon2-olon1
dimlat=olat2-olat1
lonk=dimx/dimlon
latk=dimy/dimlat
lokx=[]
loky=[]
print(olon1, olat1, olon2, olat2)
for n in range (0, len(data2)):
lon0=data2[n,1]
lat0=data2[n,0]
print(lon0, lat0)
x=int((lon0-olon1)*lonk)
y=dimy-int((lat0-olat1)*latk)
#x=int((lon0-olon1)*lonk)
#y=int((lat0-olat1)*latk)
lokx.append(x)
loky.append(y)
print(lokx)
print(loky)
lenu=len(data2)
sites=[]
for n in range (0, lenu):
x=lokx[n]
y=loky[n]
#print(x,y)
pp=(x,y)
sites.append(pp)
print(sites)
- experimental only auto-radius ver 2 ...
xk=0
yk=0
for n in range (1, lenu):
xk=lokx[n]+xk
yk=loky[n]+yk
xk=xk/lenu
yk=yk/lenu
x00=lokx[0]
y00=loky[0]
xr=0
yr=0
for n in range (1, lenu):
xr2=lokx[n]-xk
yr2=loky[n]-yk
xr=xr+xr2
yr=yr+yr2
xr=xr/(lenu)
yr=yr/(lenu)
rr=int(math.sqrt(xr*xr+yr*yr))
print(rr)
buffer_dist =rr
- buffer_dist =0
- check this
- quit(-1)
p = 0
def phi( x, y, i, j ):
if abs(x-i) + abs(y-j) > buffer_dist:
return 1
else:
return 0
def phi2( x, y, i, j ):
if (math.sqrt( abs(x-i)*abs(x-i) + abs(y-j)*abs(y-j)) ) > buffer_dist:
return 1
else:
return 0
- rossmo, manhattan distance
- for x in range(dimx):
- for y in range(dimy):
- for site in sites:
- i = site[0]
- j = site[1]
- characteristic = phi(x, y, i, j)
- manhattan_dist = abs(x-i) + abs(y-j)
- try:
- p += characteristic / pow(manhattan_dist, param_f)
- except ZeroDivisionError:
- pass
- n = (1-characteristic)*pow(buffer_dist, (param_g-param_f))
- d = (2*buffer_dist)-characteristic
- d = pow(d, param_g)
- rosmo1[y,x]=p ## tsek it!
- p = 0
#print("")
- simple distance function
p=0
- for x in range(dimx):
- for y in range(dimy):
- for site in sites:
- i = site[0]
- j = site[1]
- ux=x-i
- uy=y-j
- if(ux==0): ux=1
- if(uy==0): uy=1
- distance=(math.sqrt(ux*ux+uy*uy))/buffer_dist
- ## normal, ring radius 1,
- p0=st.norm.pdf(distance,1,0.5)
- #p0=math.log(math.pow(distance, -1))
- p=p+p0
- rosmo1[y,x]=p ## tsek it!
- p = 0
for x in range(dimx):
for y in range(dimy):
p0=0
for site in sites:
i = site[0]
j = site[1]
ux=x-i
uy=y-j
if(ux==0): ux=1
if(uy==0): uy=1
distance=(math.sqrt(ux*ux+uy*uy))/buffer_dist
## normal, ring radius 1,
#p=st.norm.pdf(distance,param_f,param_g)
p=0
#if(distance<0):
# #dis2=1-distance
# dis2=distance
# p=math.exp(-dis2/param_g)
#p0=math.log(math.pow(distance, -1))
#if(p>p0): p0=p+p0
#p=math.exp(-distance)
if(distance<=1): p=math.exp(-abs(1-distance)/param_g)
if(distance>1): p=math.exp(-abs(1-distance)/param_f)
p0=p0+p
rosmo1[y,x]=p0
plt.imshow(rosmo1)
plt.show()
- quit(-1)
imr=np.copy(rosmo1)
imb=np.copy(rosmo1)
img=np.copy(rosmo1)
ima=np.copy(rosmo1)
imr=imr*1
imb=imb*1
img=img*0
ima=255-imr
ima2=ima/256
rosmo2=np.dstack((imr, img, imb, ima))
- rosmo2=np.power(rosmo2, 1.5)*10
deltalon=dimlon/dimx
deltalat=dimlat/dimy
lontab=[]
lattab=[]
rostab=[]
for iy in range(0, dimy-1):
tlat=olat2-deltalat*iy
for ix in range(0, dimx-1):
tlon=olon1+deltalon*ix
tros=rosmo1[iy,ix]
lontab.append(tlon)
lattab.append(tlat)
rostab.append(tros)
lontab=np.asarray(lontab)
lattab=np.asarray(lattab)
rostab=np.asarray(rostab)
print (np.shape(lontab))
print (np.shape(lattab))
print (np.shape(rostab))
- quit(-1)
vmin=np.min(rostab)
vmax=np.max(rostab)
lon_arr = np.linspace(np.min(lontab), np.max(lontab), dimx)
lat_arr = np.linspace(np.min(lattab), np.max(lattab), dimy)
lon_mesh, lat_mesh = np.meshgrid(lon_arr, lat_arr)
ros_mesh = griddata((lontab, lattab), rostab, (lon_mesh, lat_mesh), method='linear')
sigma = [5, 5]
- ros_mesh = sp.ndimage.filters.gaussian_filter(ros_mesh, sigma, mode='constant')
levels=18
- colors = ['blue','royalblue', 'navy','pink', 'mediumpurple', 'darkorchid', 'plum', 'm', 'mediumvioletred', 'palevioletred', 'crimson',
- 'magenta','pink','red','yellow','orange', 'brown','green', 'darkgreen']
colors = ['black','black']
levels = len(colors)
cm = branca.colormap.LinearColormap(colors, vmin=vmin, vmax=vmax).to_step(levels)
- levels=10
- levels=[1,10,100,200]
levels=32
- print()
- contourf = plt.contourf(lon_mesh, lat_mesh, ros_mesh, levels, alpha=0.75, colors=colors, vmin=vmin, vmax=vmax)
- geojson = geojsoncontour.contourf_to_geojson(contourf=contourf,min_angle_deg=0.01,ndigits=5,stroke_width=0.5,fill_opacity=0.5)
- quit(-1)
map = folium.Map(location=[kenterlat1, kenterlon1], TileProvider='OpenStreetMap', zoom_start=16, control_scale = True,)
- map = folium.Map(location=[kenterlat1, kenterlon1], TileProvider='Stamen Toner', zoom_start=16, control_scale = True,)
- map = folium.Map(location=[kenterlat1, kenterlon1], TileProvider='stamentoner’', zoom_start=16)
- plugins.HeatMap(data, radius = 50, min_opacity = 0.5, max_val = 1,gradient={.01: 'blue', 0.5: 'yellow', 1: 'red'}).add_to(map)
- Plot the contour on Folium map
- folium.GeoJson(geojson,style_function=lambda x: {'color':"#f0f000f",'weight':0.5,'fillColor': None,'opacity': 0.5,}).add_to(map)
- folium.GeoJson( geojson, ).add_to(map)
folium.raster_layers.ImageOverlay(rosmo2,
[[ olat1, olon1],[ olat2, olon2]],
opacity=0.5,
).add_to(map)
folium.LayerControl().add_to(map)
latx1=data[0][0]
lonx1=data[0][1]
folium.Circle([latx1, lonx1], 45, fill_color="grey", opacity=1.0,color = 'grey', fill=True).add_child(folium.Popup('Hauta')).add_to(map)
folium.map.Marker(
[latx1 + 0.0001, lonx1 - 0.0001],
icon=folium.DivIcon(
icon_size=(150,36),
icon_anchor=(0,0),
html='
%s
' % "Hauta",
)
).add_to(map)
latx1=data[1][0]
lonx1=data[1][1]
folium.Circle([latx1, lonx1], 45, fill_color="blue", opacity=1.0,color = 'blue', fill=True).add_child(folium.Popup('Pyörä')).add_to(map)
folium.map.Marker(
[latx1 + 0.0001, lonx1 - 0.0001],
icon=folium.DivIcon(
icon_size=(150,36),
icon_anchor=(0,0),
html='
%s
' % "Pyörä",
)
).add_to(map)
map.fit_bounds([sw1, ne1])
- map.save("rossmotest1.html")
delay=5
- Save the map as an HTML file
fn='testmap.html'
tmpurl='file://{path}/{mapfile}'.format(path=os.getcwd(),mapfile=fn)
map.save(fn)
- quit(-1)
- Open a browser window...
browser = webdriver.Firefox()
- ..that displays the map...
browser.get(tmpurl)
- Give the map tiles some time to load
time.sleep(delay)
- Grab the screenshot
browser.save_screenshot('testmap.png')
- Close the browser
browser.quit()
Licensing
[edit]![w:en:Creative Commons](https://upload.wikimedia.org/wikipedia/commons/thumb/7/79/CC_some_rights_reserved.svg/90px-CC_some_rights_reserved.svg.png)
![attribution](https://upload.wikimedia.org/wikipedia/commons/thumb/1/11/Cc-by_new_white.svg/24px-Cc-by_new_white.svg.png)
![share alike](https://upload.wikimedia.org/wikipedia/commons/thumb/d/df/Cc-sa_white.svg/24px-Cc-sa_white.svg.png)
- You are free:
- to share – to copy, distribute and transmit the work
- to remix – to adapt the work
- Under the following conditions:
- attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- share alike – If you remix, transform, or build upon the material, you must distribute your contributions under the same or compatible license as the original.
File history
Click on a date/time to view the file as it appeared at that time.
Date/Time | Thumbnail | Dimensions | User | Comment | |
---|---|---|---|---|---|
current | 13:42, 30 August 2023 | ![]() | 341 × 741 (116 KB) | Merikanto (talk | contribs) | Uploaded own work with UploadWizard |
You cannot overwrite this file.
File usage on Commons
There are no pages that use this file.