OSGP: Measuring Geographic Distributions – Mean Center

(Open Source Geospatial Python)

The ‘What is it?’

The Mean Center is the average X coordinate and Y coordinate for all features in a study area and is the simplest descriptor of a geographic distribution. The Mean Center is generally used to track the changes in a features distribution over time and can also be used to compare the distribution of multiple features.

The Mean Center is also known as the Geographic Center or Center of Concentration for a set of features.

You would calculate the Mean Center for features where there is no travel interaction between the Center and the features of the study. Basically, use it for a study where each event that happens is a recorded location, for example a burglary for crime analysis, or the sighting of wombat for wildlife studies.

The Formula!

Mean Center Formula

For Point features the X and Y coordinates of each feature is used, for Polygons the centroid of each feature represents the X and Y coordinate to use, and for Linear features the mid-point of each line is used for the X and Y coordinate.

The Code…

from osgeo import ogr
from shapely.geometry import MultiLineString
from shapely import wkt
import numpy as np
import sys

## set the driver for the data
driver = ogr.GetDriverByName("FileGDB")
## path to the FileGDB
gdb = r"C:\Users\Glen B\Documents\my_geodata.gdb"
## ope the GDB in write mode (1)
ds = driver.Open(gdb, 1)

## input layer
input_lyr_name = "Birmingham_Burglaries_2016"

## the output layer
output_fc = "{0}_mean_center".format(input_lyr_name)

## reference the layer using the layers name
if input_lyr_name in [ds.GetLayerByIndex(lyr_name).GetName() for lyr_name in range(ds.GetLayerCount())]:
    lyr = ds.GetLayerByName(input_lyr_name)
    print "{0} found in {1}".format(input_lyr_name, gdb)

## delete the output feature class if it already exists
if output_fc in [ds.GetLayerByIndex(lyr_name).GetName() for lyr_name in range(ds.GetLayerCount())]:
    print "Deleting: {0}".format(output_fc)

    ## assess the geometry of the input feature class
    first_feat = lyr.GetFeature(1)
    ## for each point or polygon in the layer 
    ## get the x and y value of the centroid 
    ## store in a numpy array
    if first_feat.geometry().GetGeometryName() in ["POINT", "MULTIPOINT", "POLYGON", "MULTIPOLYGON"]:
        xy_arr = np.ndarray((len(lyr), 2), dtype=np.float)
        for i, pt in enumerate(lyr):
            ft_geom = pt.geometry()
            xy_arr[i] = (ft_geom.Centroid().GetX(), ft_geom.Centroid().GetY())

    ## for lineear we get the midpoint of a line
    elif first_feat.geometry().GetGeometryName() in ["LINESTRING", "MULTILINESTRING"]:
        xy_arr = np.ndarray((len(lyr), 2), dtype=np.float)
        for i, ln in enumerate(lyr):
            line_geom = ln.geometry().ExportToWkt()
            shapely_line = MultiLineString(wkt.loads(line_geom))
            midpoint = shapely_line.interpolate(shapely_line.length/2)
            xy_arr[i] = (midpoint.x, midpoint.y)

## exit gracefully if unknown geometry or input contains no geometry
except Exception:
    print "Unknown geometry for {0}".format(input_lyr_name)

avg_x, avg_y = np.mean(xy_arr, axis=0)

print "Mean Center: {0}, {1}".format(avg_x, avg_y)

## create a new point layer with the same spatial ref as input layer
out_lyr = ds.CreateLayer(output_fc, lyr.GetSpatialRef(), ogr.wkbPoint)

## define and create new fields to hold the mean center coordinates
x_fld = ogr.FieldDefn("X", ogr.OFTReal)
y_fld = ogr.FieldDefn("Y", ogr.OFTReal)

## create a new point geom for the mean center
pnt = ogr.Geometry(ogr.wkbPoint)
pnt.AddPoint(avg_x, avg_y)

## add the mean center point to the new layer with attributes
feat_dfn = out_lyr.GetLayerDefn()
feat = ogr.Feature(feat_dfn)
feat.SetField("X", avg_x)
feat.SetField("Y", avg_y)

print "Created: {0}".format(output_fc)

## free up resources
del ds, lyr, first_feat, feat, out_lyr

I’d like to give credit to Logan Byers from GIS StackExchange who aided in speeding up the computational time using NumPy and for forcing me to begin learning the wonders of NumPy.

The Example:

I downloaded crime data from DATA.POLICE.UK for the West Midlands Police from January 2016 to December 2016. I used some Python to extract just the Burglary data and made this into a feature class in the File GDB. Next, I downloaded OS Boundary Line data and clipped the Burglary data to just Birmingham. Everything was now in place to find the Mean Center of all burglaries for Birmingham in 2016. (see The Other Scripts section at the bottom of this post for processing the data)


Running the script from The Code section above calculates the Mean Center of all burglaries for 2016 and created a point feature class in the File GDB.


OSGP Mean Center:     407926.695396, 286615.428507
ArcGIS Mean Center:    407926.695396, 286615.428507

What’s Next?…

Central Feature

The Resources:

ESRI Guide to GIS Volume 2: Chapter 2 (I highly recommend this book)
see book review here.

Geoprocessing with Python

Python GDAL/OGR Cookbook

Setting up GDAL/OGR with FileGDB Driver for Python on Windows

< The Other Scripts >

1. Extract Burglary Data for West Midlands

import csv, os
from osgeo import ogr, osr

## set the driver for the data
driver = ogr.GetDriverByName("FileGDB")

## path to the FileGDB
gdb = r"C:\Users\Glen B\Documents\my_geodata.gdb"

## ope the GDB in write mode (1)
ds = driver.Open(gdb, 1)

## the coordinates in the csv files are lat/long
source = osr.SpatialReference()

## we need the data in British National Grid
target = osr.SpatialReference()

transform = osr.CoordinateTransformation(source, target)

## set the output fc name
output_fc = "WM_Burglaries_2016"

## if the output fc already exists delete it
if output_fc in [ds.GetLayerByIndex(lyr_name).GetName() for lyr_name in range(ds.GetLayerCount())]:
    print "Deleting: {0}".format(output_fc)

out_lyr = ds.CreateLayer(output_fc, target, ogr.wkbPoint)

## define and create new fields
mnth_fld = ogr.FieldDefn("Month", ogr.OFTString)
rep_by_fld = ogr.FieldDefn("Reported_by", ogr.OFTString)
fls_wthn_fld = ogr.FieldDefn("Falls_within", ogr.OFTString)
loc_fld = ogr.FieldDefn("Location", ogr.OFTString)
lsoa_c_fld = ogr.FieldDefn("LSOA_code", ogr.OFTString)
lsoa_n_fld = ogr.FieldDefn("LSOA_name", ogr.OFTString)
crime_fld = ogr.FieldDefn("Crime_type", ogr.OFTString)
outcome_fld = ogr.FieldDefn("Last_outcome", ogr.OFTString)


## where the downloaded csv files reside
root_folder = r"C:\Users\Glen B\Documents\Crime"

## for each csv
for root,dirs,files in os.walk(root_folder):
    for filename in files:
        if filename.endswith(".csv"):
            csv_path = "{0}\\{1}".format(root, filename)
            with open(csv_path, "rb") as csvfile:
                reader = csv.reader(csvfile, delimiter=",")
                ## create a point with attributes for each burglary
                for row in reader:
                    if row[9] == "Burglary":
                        pnt = ogr.Geometry(ogr.wkbPoint)
                        pnt.AddPoint(float(row[4]), float(row[5]))
                        feat_dfn = out_lyr.GetLayerDefn()
                        feat = ogr.Feature(feat_dfn)
                        feat.SetField("Month", row[1])
                        feat.SetField("Reported_by", row[2])
                        feat.SetField("Falls_within", row[3])
                        feat.SetField("Location", row[6])
                        feat.SetField("LSOA_code", row[7])
                        feat.SetField("LSOA_name", row[8])
                        feat.SetField("Crime_type", row[9])
                        feat.SetField("Last_outcome", row[10])

del ds, feat, out_lyr

2. Birmingham Burglaries Only

from osgeo import ogr

## required drivers
shp_driver = ogr.GetDriverByName("ESRI Shapefile")
gdb_driver = ogr.GetDriverByName("FileGDB")

## input boundary shapefile and file gdb
shapefile = r"C:\Users\Glen B\Documents\Crime\Data\GB\district_borough_unitary_region.shp"
gdb = r"C:\Users\Glen B\Documents\my_geodata.gdb"

## open the shapefile in read mode and gdb in write mode
shp_ds = shp_driver.Open(shapefile, 0)
gdb_ds = gdb_driver.Open(gdb, 1)

## reference the necessary layers
shp_layer = shp_ds.GetLayer(0)
gdb_layer = gdb_ds.GetLayerByName("WM_Burglaries_2016")

## filter the shapefile
shp_layer.SetAttributeFilter("NAME = 'Birmingham District (B)'")

## set the name for the output feature class
output_fc = "Birmingham_Burglaries_2016"

## if the output already exists then delete it
if output_fc in [gdb_ds.GetLayerByIndex(lyr_name).GetName() for lyr_name in range(gdb_ds.GetLayerCount())]:
    print "Deleting: {0}".format(output_fc)

## create an output layer
out_lyr = gdb_ds.CreateLayer(output_fc, shp_layer.GetSpatialRef(), ogr.wkbPoint)

## copy the schema from the West Midlands burglaries
## and use it for the Birmingham burglaries
lyr_def = gdb_layer.GetLayerDefn()
for i in range(lyr_def.GetFieldCount()):
    out_lyr.CreateField (lyr_def.GetFieldDefn(i))

## only get burglaries that intersect the Birmingham region
for shp_feat in shp_layer:
    print shp_feat.GetField("NAME")
    birm_geom = shp_feat.GetGeometryRef()
    for gdb_feat in gdb_layer:
        burglary_geom = gdb_feat.GetGeometryRef()
        if burglary_geom.Intersects(birm_geom):
            feat_dfn = out_lyr.GetLayerDefn()
            feat = ogr.Feature(feat_dfn)

            ## populate the attribute table
            for i in range(lyr_def.GetFieldCount()):
                feat.SetField(lyr_def.GetFieldDefn(i).GetNameRef(), gdb_feat.GetField(i))
            ## create the feature

del shp_ds, shp_layer, gdb_ds, gdb_layer

The Usual 🙂

As always please feel free to comment to help make the code more efficient, highlight errors, or let me know if this was of any use to you.

Setting up GDAL/OGR with FileGDB Driver for Python on Windows

I have decided to venture into the world of GDAL/OGR with Python with my main motivation to mimic some tools from ArcGIS for Desktop. I am hoping that this will help me to improve on a few fronts; my Python coding, increased knowledge regarding open source geospatial libraries, and to better understand the algorithms that churn away behind the scenes when you click a button in a GUI based GIS and perform some sort of geoprocessing or data analysis.

I mainly work with ESRI File Geodatabases and while I know this is not open source ESRI have an API in place to read and write to a gdb via GDAL/OGR. The first step is to setup what I need to start my journey for learning GDAL/OGR with Python for Windows. I will also install a few libraries that will help speed up some computations for more efficient geoprocessing.

I am using…
Python 2.7.13 32bit on Windows 7 Professional

1. Download and Install Microsoft Visual C++ 2008 Service Pack

Click here to download and the install.

microsoft_visual c++

2. Go to Christoph Gohlke’s website and download the GDAL wheel.

Grab the GDAL whl file. I downloaded GDAL‑2.1.3‑cp27‑cp27m‑win32.whl
Open the command prompt, change directory to where the whl was downloaded and use pip to install.

pip install "GDAL‑2.1.3‑cp27‑cp27m‑win32.whl"

gdal_whl installation

3. Get the File Geodatabase API from ESRI (you will need an ESRI account)

Go to ESRI Dowloads and download File Geodatabase API 1.3 version for Windows (Visual Studio 2008). This will be a zip folder. Open the contents of the API zipped folder and extract FileGDBAPI.dll from the bin folder to


or wherever your site-package folder resides. Just make sure to extract it to osgeo.

4. Create a New Variable in Environmental Variables

In Advanced System Settings create a new Environmental Variable called GDAL_DRIVER_PATH and set the path to the osgeo folder in Step 5.

5. Open __init__.py from osgeo…

… and uncomment line 10.


Save the file.

6. Test the setup

Open a Python interpreter and test using…

test gdal setup

If you do not get an errors like the screenshot above then setup has been successful.

OPTIONAL: these will be used in some capacity for scripting geoprocessing,

7. Download numpy + mkl wheel from the brilliant website of Christoph Gohlke

Click here and download the necessary whl file. For my setup I have downloaded numpy‑1.11.3+mkl‑cp27‑cp27m‑win32.whl 
Open up the command prompt and change directory to where the downloaded file resides. Use pip to install.

pip install "numpy‑1.11.3+mkl‑cp27‑cp27m‑win32.whl"


8. Install SciPy

Back we go to Gohlke repository and to the SciPy Wheels. Here, I have downloaded scipy‑0.19.0‑cp27‑cp27m‑win32.whl
Open up the command prompt if you have closed it after Step 1 and change directory to where the downloaded file can be found.
Use pip to install.

pip install "scipy‑0.19.0‑cp27‑cp27m‑win32.whl"


9. Install Shapely

You got it, go back to Gohlke and download the Shapely whl file. I grabbed Shapely‑1.5.17‑cp27‑cp27m‑win32.whl. Use pip to install similar to Steps 7 and 8.


Now to immerse myself in learning mode and put GDAL/OGR to some use. Check out OSGP#1.1: Measuring Geographic Distributions – Mean Center for the first attempt.