GeoGnomo is an open-source project exploring various forms of geocoding for use in Smart Ledgers (aka blockchains with embedded computer code). The GeoGnomo project focuses on memorable systems that provide easy ways to aggregate areas together, as well as giving users some clues to location and distance and scale. GeoGnomo consists of three systems for geostamping: Quaternary Triangular System, a Quaternary Rectangular System, and a Variable Rectangular System. Each can be used to record geographic areas into a blockchain. We have provided the source code and an online translation from latitude & longitude areas to geocodes and back. Thus, blockchain applications have almost an instant ‘global post code’ or ‘global zip code’ system. By using consistent geocoding data retrieval is easier. Users can share information easily – “tell the drone to come to F49PUR9B7-20”, a resolution of 7.6 metres.
We believe the principal qualities of a good geocode are:
Memorability - It should be compact and memorable
Aggregation - A coding system should be able to describe comparably a variety of area sizes and structures, both natural and human, such as forests, beaches, buildings, sports grounds, country borders, etc.
Proximity - Similar codes should represent similar locations, so that people exchanging codes can roughly understand the distance and relationship between them.
Scale - Users should have control over the precision.
GeoGnomo encompasses four different approaches designed to provide effective methods for geocoding:
Variable Rectangular System (VRS):
In the Variable Rectangular System codes are generated from a rectangular area, that may be specified through a ‘click and drag’ selection and represent the selected area
To explore VRS in action click here!
Quaternary Rectangular System (QRS):
Quaternary Rectangular System defines the level 0 grid by dividing latitude into 3 bands and longitude into 6 bands, resulting in eighteen 60 by 60 degree squares that can be subdivided with no exceptions.
To explore QRS in action click here!
Quaternary Triangular System (QTS)
The Quaternary Triangle System divides the globe into a fixed grid of triangles and assigns a unique geocode to each triangle. Codes are generated from a latitude/longitude coordinate pair and a specified level n, which determines the scale of the grid. The code generated describes an area that contains the specified point.
To explore QTS in action click here!
Quaternary UTM System (QUTMS) (Experimental)
Quaternary UTM System uses the same quaternary trail method as QTS but defines its level 0 grid according to the Universal Transverse Mercator (UTM) projection, combined with the Military Grid Reference System (MGRS). It also generates codes from a single point and a specified scale level, with the code representing the area that the point lies in.
To explore QUTMS in action click here!
Z/Yen presents four geocoding system as open source for further advancement of algorithm and its proper and optimized implementation. Geognomo itself is a Django web application and is integrated with google map api to display and work with maps in front-end. The open source code acts as basic back-end code which generated all the details required to display it in any front-end of your choice.
Open sourced Geocoding system is a python project which lies under geocoding source folder. Its structure is as follows:
- base32.json: It is the json file which acts as base32 converter to alphabets. Alphabets are used for simplicity and memoriability in Geognomo coding systems.
- base32_data.py: It loads base32.json file from source directory and returns json data for future manipulation.
- example.py: If you want a quick start into GeoGnomo, have a look at this python file. It calls geo-coding functions and prints result to give you an example of how it works and can be implemented.
- geocode_decoder: This is the decoder file of QTM, QUTMS geo-coding system.
- geocode_encoder.py: This file acts as encoder for three different geo-coding system. QTM, QRS and QUTMS.
- geocoding.py: This is the bridge between geo-coding systems and front-end. It communicates with encoder and decoder of different geo coding systems and returns the result in json format.
- integer2base.py: This function converts integer into the binary system.
- interactive_example.py: This file contains an interactive example: asks for user input and prints different geo-codes based on the input.
- qrs_decoder: This is the decoder file for QRS geo-coding system.
- triangle.py: This file is the module used inside QTM code to manipulate triangle.
- vrs_decoder: Decoder for VRS
- vrs_encoder: Encoder for VRS
This is a python project therefore python needs to be installed into the local machine. Git and numpy packages are also needed. If you have pip installed, you can install numpy as follows:
# install numpy using pip:
pip install numpy
If you don't have pip installed look documentation here.
After you install numpy just clone the project into local machine.
git clone https://github.com/ZYenGroupLimited/Geognomo.git
cd Geognomo/
Running example files:
# you can run example.py to see how it works.
python example.py
# if you want to run interactive example:
python interactive_example.pyFind the meaning of the words used as key in returned json data:
- '__GeoGnomo_Code' : the shareable code which is generated using QTM/QRS/QUTMS/VRS geo-coding system.
- 'LatLon_' is the latitude, longitude of vertex(A/B/C/D) of triangle/ rectangle which enclosed the given latitude, longitude.
- 'Top_LatLon_' is the latitude, longitude of vertex(A/B/C/D) of triangle/ rectangle which lies at top of main triangle/rectangle.
- 'Left_LatLon_' is the latitude, longitude of vertex(A/B/C/D) of triangle/ rectangle which lies on left of main triangle/rectangle.
- 'Right_LatLon_' is the latitude, longitude of vertex(A/B/C/D) of triangle/ rectangle which lies on right of main triangle/rectangle.
- 'Bottom_LatLon_' is the latitude, longitude of vertex(A/B/C/D) of triangle/ rectangle which lies on bottom of main triangle/rectangle.
- 'area' is the total area of the enclosed by main triangle/rectangle.
- '_Base32_Code' is the base 32 alphabetic code for given latitude, longitude in that level produced by QTM/QRS/QUTMS/VRS encoding.
- '_Neighbor' gives the QTM_Base32_Code for neighbors of main triangle/rectangle.
You can run QTM code system as shown:
import geocoding
#geocoding.LatLonToIcosahedronID(latitude, longitude, altitude, level)
latlon2icos = geocoding.LatLonToIcosahedronID(51.499207299999995, -0.08800609999999999, 0, 5)
print latlon2icos
#Returned Json value. Example 1:
{
"QTM_Quarternary_Code": "5,31133",
"Bottom_LatLonA": "(50.353157,-3.6)",
"Bottom_LatLonB": "(50.353157,0.0)",
"Bottom_LatLonC": "(48.370815,-3.428571)",
"Top_LatLonA": "N/A",
"Top_LatLonC": "N/A",
"Top_LatLonB": "N/A",
"QTM_GeoGnomo_Code": "F49",
"Left_LatLonB": "(52.335499,0.0)",
"Left_LatLonC": "(50.353157,-3.6)",
"Right_Neighbor": "Bravo,Sierra,4",
"area": 28200.71915628558,
"LatLonA": "(50.353157,-3.6)",
"LatLonC": "(50.353157,0.0)",
"LatLonB": "(52.335499,0.0)",
"QTM_Base32_Code": "Foxtrot,4,9",
"Left_Neighbor": "Foxtrot,4,6",
"Left_LatLonA": "(52.335499,-3.789474)",
"IJ": "(13,39)",
"LatLonCentral": "(51.013938,-1.220339)",
"Right_LatLonA": "(50.353157,-0.0)",
"Right_LatLonB": "(52.335499,0.0)",
"Right_LatLonC": "(50.353157,3.6)",
"Top_Neighbor": "N/A",
"Bottom_Neighbor": "Foxtrot,4,Papa"
}You can run QTM decoding as shown:
import geocoding
#geocoding.IcosIDToLatLon(QTM_GeoGnomo_Code, level)
icos2latlon = geocoding.IcosIDToLatLon('F49', 5)
print icos2latlon
#Returned Json value. Example 2:
{
"QTM_Quarternary_Code": "5,31133",
"Bottom_LatLonA": "(50.353157,-3.6)",
"Bottom_LatLonB": "(50.353157,0.0)",
"Bottom_LatLonC": "(48.370815,-3.428571)",
"Top_LatLonA": "N/A",
"Top_LatLonC": "N/A",
"Top_LatLonB": "N/A",
"Left_LatLonA": "(52.335499,-3.789474)",
"Left_LatLonB": "(52.335499,0.0)",
"Left_LatLonC": "(50.353157,-3.6)",
"Right_Neighbor": "Bravo,Sierra,4",
"area": 28200.71915628558,
"altitude": "N/A",
"LatLonA": "(50.353157,-3.6)",
"LatLonC": "(50.353157,0.0)",
"LatLonB": "(52.335499,0.0)",
"Left_Neighbor": "Foxtrot,4,6",
"IJ": "(13,39)",
"LatLonCentral": "(51.013938,-1.220339)",
"Right_LatLonA": "(50.353157,-0.0)",
"Right_LatLonB": "(52.335499,0.0)",
"Right_LatLonC": "(50.353157,3.6)",
"Top_Neighbor": "N/A",
"Bottom_Neighbor": "Foxtrot,4,Papa"
}You can run QRS code system as shown:
import geocoding
#geocoding.LatLonToQrsID(latitude, longitude, altitude, level)
latlon2qrs = geocoding.LatLonToQrsID(51.499207299999995, -0.08800609999999999, 0, 5)
print latlon2qrs
# Returned Json value.Example 3
{
"Bottom_LatLonA": "(48.75,-1.875)",
"Bottom_LatLonB": "(50.625,-1.875)",
"Bottom_LatLonC": "(50.625,0.0)",
"Bottom_LatLonD": "(48.75,0.0)",
"Top_LatLonD": "(52.5,0.0)",
"Top_LatLonA": "(52.5,-1.875)",
"Top_LatLonC": "(54.375,0.0)",
"Top_LatLonB": "(54.375,-1.875)",
"Left_LatLonA": "(50.625,-3.75)",
"Left_LatLonB": "(52.5,-3.75)",
"Left_LatLonC": "(52.5,-1.875)",
"Left_LatLonD": "(50.625,-1.875)",
"Right_Neighbor": "Bravo,Romeo,Alpha",
"area": 27081.57217945275,
"QTM32Code": "Golf,5,Victor",
"LatLonD": "(50.625,0.0)",
"LatLonA": "(50.625,-1.875)",
"LatLonC": "(52.5,0.0)",
"LatLonB": "(52.5,-1.875)",
"QRS_GeoGnomo_Code": "G5V",
"Left_Neighbor": "Golf,5,Uniform",
"LatLonCenter": "(51.5625,-0.9375)",
"Right_LatLonD": "(50.625,1.875)",
"Right_LatLonA": "(50.625,0)",
"Right_LatLonB": "(52.5,0)",
"Right_LatLonC": "(52.5,1.875)",
"QTMCode": "6,31311",
"Top_Neighbor": "Golf,4,9",
"Bottom_Neighbor": "Golf,5,X-ray"
}You can run QRS decoding as shown:
import geocoding
#geocoding.QRSIDToLatLon(QRS_GeoGnomo_Code, level)
qrs2latlon = geocoding.QRSIDToLatLon('G5V', 5)
print qrs2latlon
#Returned Json value.Example 4
{
"Bottom_LatLonA": "(48.75,-1.875)",
"Bottom_LatLonB": "(50.625,-1.875)",
"Bottom_LatLonC": "(50.625,0.0)",
"Bottom_LatLonD": "(48.75,0.0)",
"Top_LatLonD": "(52.5,0.0)",
"Top_LatLonA": "(52.5,-1.875)",
"Top_LatLonC": "(54.375,0.0)",
"Top_LatLonB": "(54.375,-1.875)",
"Left_LatLonA": "(50.625,-3.75)",
"Left_LatLonB": "(52.5,-3.75)",
"Left_LatLonC": "(52.5,-1.875)",
"Left_LatLonD": "(50.625,-1.875)",
"Right_Neighbor": "Bravo,Romeo,Alpha",
"area": 27081.57217945275,
"altitude": "N/A",
"LatLonD": "(50.625,0.0)",
"LatLonA": "(50.625,-1.875)",
"LatLonC": "(52.5,0.0)",
"LatLonB": "(52.5,-1.875)",
"Left_Neighbor": "Golf,5,Uniform",
"Right_LatLonD": "(50.625,1.875)",
"LatLonCentral": "(51.5625,-0.9375)",
"Right_LatLonA": "(50.625,0)",
"Right_LatLonB": "(52.5,0)",
"Right_LatLonC": "(52.5,1.875)",
"Top_Neighbor": "Golf,4,9",
"Bottom_Neighbor": "Golf,5,X-ray"
}You can run QUTMS code system as shown:
import geocoding
#geocoding.LatLonToQUTMSID(latitude, longitude, altitude, level)
latlon2qutms = geocoding.LatLonToQUTMSID(51.499207299999995, -0.08800609999999999, 0, 5)
print latlon2qutms
#Returned Json value.Example 5
{
"Bottom_LatLonA": "(51.0,-0.1875)",
"Bottom_LatLonB": "(51.25,-0.1875)",
"Bottom_LatLonC": "(51.25,0.0)",
"Bottom_LatLonD": "(51.0,0.0)",
"Top_LatLonD": "(51.5,0.0)",
"Top_LatLonA": "(51.5,-0.1875)",
"Top_LatLonC": "(51.75,0.0)",
"Top_LatLonB": "(51.75,-0.1875)",
"Left_LatLonA": "(51.25,0)",
"Left_LatLonB": "(51.5,0)",
"Left_LatLonC": "(51.5,0.1875)",
"Left_LatLonD": "(51.25,0.1875)",
"Right_Neighbor": "30U-4,6",
"area": 362.59046108503685,
"QTM32Code": "30U-4,7",
"LatLonD": "(51.25,0.0)",
"LatLonA": "(51.25,-0.1875)",
"LatLonC": "(51.5,0.0)",
"LatLonB": "(51.5,-0.1875)",
"Left_Neighbor": "31U-Quebec,India",
"Bottom_Neighbor": "30U-4,9",
"LatLonCenter": "(51.375,-0.09375)",
"Right_LatLonD": "(51.25,-0.1875)",
"Right_LatLonA": "(51.25,-0.375)",
"Right_LatLonB": "(51.5,-0.375)",
"Right_LatLonC": "(51.5,-0.1875)",
"QTMCode": "30U,31131",
"Top_Neighbor": "30U-4,X-ray",
"QUTMS_GeoGnomo_Code": "30U-47"
}You can run QUTMS decoding as shown:
import geocoding
#geocoding.QUTMSIDToLatLon(QUTMS_GeoGnomo_Code, level)
qutms2latlon = geocoding.QUTMSIDToLatLon('30U-47', 5)
print qutms2latlon
#Returned Json value.Example 6
{
"Bottom_LatLonA": "(51.0,-0.1875)",
"Bottom_LatLonB": "(51.25,-0.1875)",
"Bottom_LatLonC": "(51.25,0.0)",
"Bottom_LatLonD": "(51.0,0.0)",
"Top_LatLonD": "(51.5,0.0)",
"Top_LatLonA": "(51.5,-0.1875)",
"Top_LatLonC": "(51.75,0.0)",
"Top_LatLonB": "(51.75,-0.1875)",
"Left_LatLonA": "(51.25,0)",
"Left_LatLonB": "(51.5,0)",
"Left_LatLonC": "(51.5,0.1875)",
"Left_LatLonD": "(51.25,0.1875)",
"Right_Neighbor": "30U-4,6",
"area": 362.59046108503685,
"altitude": "N/A",
"LatLonD": "(51.25,0.0)",
"LatLonA": "(51.25,-0.1875)",
"LatLonC": "(51.5,0.0)",
"LatLonB": "(51.5,-0.1875)",
"Left_Neighbor": "31U-Quebec,India",
"LatLonCenter": "(51.375,-0.09375)",
"Right_LatLonD": "(51.25,-0.1875)",
"Right_LatLonA": "(51.25,-0.375)",
"Right_LatLonB": "(51.5,-0.375)",
"Right_LatLonC": "(51.5,-0.1875)",
"Top_Neighbor": "30U-4,X-ray",
"Bottom_Neighbor": "30U-4,9"
}VRS coding system takes two points as inputs, so we need two (latitude, longitude) combinations. You can run VRS code system as shown:
import geocoding
#geocoding.LatLonToVRS(latitude1, longitude1, latitude2, longitude2)
latlon2vrs = geocoding.LatLonToVRS(51.49518997210978, -0.11363044381141663, 51.505876019041246, -0.09002700448036194)
print latlon2vrs
#Returned Json value.Example 7
{
"LAT1": 51.49518997210978,
"area": 1.7742498857804638,
"LAT2": 51.505876019041246,
"VRS_GeoGnomo_Code": "RUYQ8VN57H",
"LatLonA": "(51.495,-0.114)",
"LatLonB": "(51.505,-0.091)",
"LON1": -0.11363044381141663,
"LON2": -0.09002700448036194
}You can run VRS decoding as shown:
import geocoding
#geocoding.VRSToLatLon(VRS_GeoGnomo_Code)
vrs2latlon = geocoding.VRSToLatLon('RUYQ8VN57H')
print vrs2latlon
#Returned Json value.Example 8
{
"LatLonB": "(51.505, -0.091)",
"LatLonA": "(51.495, -0.114)",
"area": 1.7742498857804638
}As GeoGnomo is an open source project, we have provided API as well. It can be integrated into any application and services. Z/Yen will keep the web API updated as new improvements are introduced in GitHub. This API is a URL based API. You will query the web url and get results in JSON format which can be further manipulated by you.
Url for Api uses are as follows: "http://geognomo.com/"
- geognomo/api/latlon2qtm/latitude/longitude/altitude/level:
- geognomo/api/qtm2latlon/QTM_GeoGnomo_Code/level:
- geognomo/api/latlon2qrs/latitude/longitude/altitude/level
- geognomo/api/qrs2latlon/QRS_GeoGnomo_Code/level
- geognomo/api/latlon2qutms/latitude/longitude/altitude/level
- geognomo/api/qutms2latlon/QUTMS_GeoGnomo_Code/level
- geognomo/api/latlon2vrs/latitude1/longitude1/latitude2/longitude2
- geognomo/api/vrs2latlon/VRS_GeoGnomo_Code/level
Simple python application to use API is as follows:
import urllib2
# for QTM Encoding:
##http://geognomo.com/geognomo/api/latlon2qtm/latitude/longitude/altitude/level
pageUrl_qtm2latlon = "http://geognomo.com/geognomo/api/latlon2qtm/51.49518997210978/-0.11363044381141663/0/5/"
response_qtm2latlon = urllib2.urlopen(pageUrl_qtm2latlon).read()
print response_qtm2latlon
## It will result exact output as shown in Example 1
## for decoding from qtm code to latitude longitude
##http://geognomo.com/geognomo/api/qtm2latlon/QTM_GeoGnomo_Code/level
pageUrl_latlon2qtm = "http://geognomo.com/geognomo/api/qtm2latlon/f49/5"
response_latlon2qtm = urllib2.urlopen(pageUrl_latlon2qtm).read()
print response_latlon2qtm
## It will result exact output as shown in Example 2
# for QRS Encoding:
##http://geognomo.com/geognomo/api/latlon2qrs/latitude/longitude/altitude/level
pageUrl_latlon2qrs = "http://geognomo.com/geognomo/api/latlon2qrs/51.49518997210978/-0.11363044381141663/0/5/"
response_latlon2qrs = urllib2.urlopen(pageUrl_latlon2qrs).read()
print response_latlon2qrs
## It will result exact output as shown in Example 3
## for decoding from QRS code to latitude longitude
##http://geognomo.com/geognomo/api/qrs2latlon/QRS_GeoGnomo_Code/level
pageUrl_qrs2latlon = "http://geognomo.com/geognomo/api/qrs2latlon/G5V/5/"
response_qrs2latlon = urllib2.urlopen(pageUrl_qrs2latlon).read()
print response_qrs2latlon
## It will result exact output as shown in Example 4
# for QUTMS Encoding:
##http://geognomo.com/geognomo/api/latlon2qutms/latitude/longitude/altitude/level
pageUrl_latlon2qutms = "http://geognomo.com/geognomo/api/latlon2qutms/51.49518997210978/-0.11363044381141663/0/5/"
response_latlon2qutms = urllib2.urlopen(pageUrl_latlon2qutms).read()
print response_latlon2qutms
## It will result exact output as shown in Example 5
## for decoding from QUTMS code to latitude longitude
##http://geognomo.com/geognomo/api/qutms2latlon/QUTMS_GeoGnomo_Code/level
pageUrl_qutms2latlon = "http://geognomo.com/geognomo/api/qutms2latlon/30U-47/5/"
response_qutms2latlon = urllib2.urlopen(pageUrl_qutms2latlon).read()
print response_qutms2latlon
## It will result exact output as shown in Example 6
# for VRS Encoding:
##http://geognomo.com/geognomo/api/latlon2vrs/latitude1/longitude1/latitude2/longitude2
pageUrl_latlon2vrs = "http://geognomo.com/geognomo/api/latlon2vrs/51.49518997210978/-0.11363044381141663/\
51.505876019041246/-0.09002700448036194/"
response_latlon2vrs = urllib2.urlopen(pageUrl_latlon2vrs).read()
print pageUrl_latlon2vrs
## It will result exact output as shown in Example 7
## for decoding from QRS code to latitude longitude
##http://geognomo.com/geognomo/api/vrs2latlon/VRS_GeoGnomo_Code/level
pageUrl_vrs2latlon = "http://geognomo.com/geognomo/api/vrs2latlon/RUYQ8VN57H/"
response_vrs2latlon = urllib2.urlopen(pageUrl_vrs2latlon).read()
print response_vrs2latlon
## It will result exact output as shown in Example 8