# OSGP: Measuring Geographic Distributions – Central Feature

(Open Source Geospatial Python)

The ‘What is it?’

The Central Feature is the point that is the shortest distance to all other points in the dataset and thus identifies the most centrally located feature. The Central Feature can be used to find the most accessible feature, for example, the most accessible school to hold a training day for teachers from schools in a given area.

The Formula!

For each feature calculate the total distance to all other features. The feature that has the shortest total distance is the Central Feature.

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

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

## input layer
input_lyr_name = "Birmingham_Secondary_Schools"

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

## for each point or polygon in the layer
## get the x and y value of the centroid
## and add them into a numpy array
try:
first_feat = lyr.GetFeature(1)
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 linear features 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()
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)

## construct NxN array, this will be the distance matrix
pt_dist_arr = np.ndarray((len(xy_arr), len(xy_arr)), dtype=np.float)

## fill the distance array
for i, a in enumerate(xy_arr):
for j, b in enumerate(xy_arr):
pt_dist_arr[i,j] = np.linalg.norm(a-b)

## sum distances for each point
summed_distances = np.sum(pt_dist_arr, axis=0)

## index of point with minimum summed distances
index_central_feat = np.argmin(summed_distances)

## position of the point with min distance
central_x, central_y = xy_arr[index_central_feat]

print "Central Feature Coords: {0}, {1}".format(central_x, central_y)

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

## define and create new fields
x_fld = ogr.FieldDefn("X", ogr.OFTReal)
y_fld = ogr.FieldDefn("Y", ogr.OFTReal)
out_lyr.CreateField(x_fld)
out_lyr.CreateField(y_fld)

## create a new point for the mean center
pnt = ogr.Geometry(ogr.wkbPoint)

## add the mean center to the new layer
feat_dfn = out_lyr.GetLayerDefn()
feat = ogr.Feature(feat_dfn)
feat.SetGeometry(pnt)
feat.SetField("X", central_x)
feat.SetField("Y", central_y)
out_lyr.CreateFeature(feat)

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.

At the moment this is significantly slower that performing the same process with ArcGIS for 20,000+ features, but more rapid for a lower amount. 1,000 features processed in 3 seconds.

The Example:

I downloaded vector data that contains polygons for schools from OS Open Map – Local that covered the West Midlands. I also downloaded OS Boundary Line data. Using Python and GDAL/OGR I extracted secondary schools from the data for Birmingham. Everything was now in place to find the Central Feature of all Secondary Schools for Birmingham. (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 coordinates of the Central Feature for all Secondary Schools and creates a point feature class in the File GDB.

OSGP Central Feature:      407726.185, 287215.1
ArcGIS Central Feature:    407726.185, 287215.1

What’s Next?

Median Center (link will be updated once post is complete)

Also see…

Mean Center

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 >

Birmingham Secondary Schools

from osgeo import ogr
import os

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

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

shp_ds = shp_driver.Open(shapefile, 0)
gdb_ds = gdb_driver.Open(gdb, 1)

## filter boundary to just Birmingham
shp_layer = shp_ds.GetLayer(0)
shp_layer.SetAttributeFilter("NAME = 'Birmingham District (B)'")

## name the output
output_fc = "Birmingham_Secondary_Schools"

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

## create the output feature class
out_lyr = gdb_ds.CreateLayer(output_fc, shp_layer.GetSpatialRef(), ogr.wkbPolygon)

## the folder that contains the data to extract Secondary Schools from
root_folder = r"C:\Users\Glen B\Documents\Schools\Vector\data"

## traverse through the folders and find ImportantBuildings files
## copy only those that intersect the Birmingham region
## transfer across attributes
count = 1
for root,dirs,files in os.walk(root_folder):
for filename in files:
if filename.endswith("ImportantBuilding.shp") and filename[0:2] in ["SP", "SO", "SJ", "SK"]:
shp_path = "{0}\\{1}".format(root, filename)
schools_ds = shp_driver.Open(shp_path, 0)
schools_lyr = schools_ds.GetLayer(0)
schools_lyr.SetAttributeFilter("CLASSIFICA = 'Secondary Education'")
lyr_def = schools_lyr.GetLayerDefn()
if count == 1:
for i in range(lyr_def.GetFieldCount()):
out_lyr.CreateField(lyr_def.GetFieldDefn(i))
count += 1
for shp_feat in shp_layer:
birm_geom = shp_feat.GetGeometryRef()

for school_feat in schools_lyr:
school_geom = school_feat.GetGeometryRef()

if school_geom.Intersects(birm_geom):
feat_dfn = out_lyr.GetLayerDefn()
feat = ogr.Feature(feat_dfn)
feat.SetGeometry(school_geom)
for i in range(lyr_def.GetFieldCount()):
feat.SetField(lyr_def.GetFieldDefn(i).GetNameRef(), school_feat.GetField(i))

out_lyr.CreateFeature(feat)
feat.Destroy()

del shp_ds, shp_layer, gdb_ds

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.

# Table or Feature Class Attributes to CSV with ArcPy (Python)

Here’s a little function for exporting an attribute table from ArcGIS to a CSV file. The function takes two arguments, these are a file-path to the input feature class or table and a file-path for the output CSV file (see example down further).

First import the necessary modules.

import arcpy, csv

Inside the function we use ArcPy to get a list of the field names.

def tableToCSV(input_tbl, csv_filepath):
fld_list = arcpy.ListFields(input_tbl)
fld_names = [fld.name for fld in fld_list]

We then open a CSV file to write the data to.

with open(csv_filepath, 'wb') as csv_file:
writer = csv.writer(csv_file)

The first row of the output CSV file contains the header which is the list of field names.

writer.writerow(fld_names)

We then use the ArcPy SearchCursor to access the attributes in the table for each row and write each row to the output CSV file.

with arcpy.da.SearchCursor(input_tbl, fld_names) as cursor:
for row in cursor:
writer.writerow(row)

And close the CSV file.

csv_file.close()

Full script example…

import arcpy, csv

def tableToCSV(input_tbl, csv_filepath):
fld_list = arcpy.ListFields(input_tbl)
fld_names = [fld.name for fld in fld_list]
with open(csv_filepath, 'wb') as csv_file:
writer = csv.writer(csv_file)
writer.writerow(fld_names)
with arcpy.da.SearchCursor(input_tbl, fld_names) as cursor:
for row in cursor:
writer.writerow(row)
print csv_filepath + " CREATED"
csv_file.close()

fc = r"C:\Users\******\Documents\ArcGIS\Default.gdb\my_fc"
out_csv = r"C:\Users\******\Documents\output_file.csv"

tableToCSV(fc, out_csv)

Feel free to ask questions, comment, or help build upon this example.

# GIS Career Advice Suggestions; my experiences & opinions.

Warning: this is a lengthy read that may borderline a rant, you have been warned!

Career advice is everywhere these days from generic all-round advice via do’s and do not’s encompassing any career to must do’s and must not’s for a more focused delineated career path. There are a few great articles out there but most, however, seem to lack the personal experience of the author, and while I wouldn’t necessarily disagree with some or any of the advice, I feel that it often does not reflect reality, well my reality anyway. With that in mind I decided to put in words some ‘suggestions’ based on my experiences to date. Notice that I have labelled these ‘suggestions’ because I don’t think it’s possible to give someone career advice without knowing their individual personal situation. If the generic career advice was golden we would be all going into interviews and employment like robots acting the same as each other. The suggestions below are not numbered and I’m not initiating that you follow them in any particular systematic way or at all in fact if you don’t feel they’re for you. You might find some of these to be enlightening with a fresh approach or on the other-hand you might completely disagree with some that go completely against the grain, you might even think that one or two are complete tripe and a terrible suggestion, but at the end of the day these are my experiences, my opinions, and what has worked for me and what I have learned along the way. So here we go…

# Be Educated

While experience stands out on a resume it is highly unlikely that your experience would be on your resume without an educational background. If you want a potential employer to take you serious for an entry level or graduate role then having the necessary education is a huge step in the right direction. Many university courses now offer GIS modules as part of a wider discipline such as environmental sciences or engineering for examples. While these modules are a good way to get acquainted with GIS software they will more than likely lack in many aspects. In particular the theory behind GIS and GIScience, the theory behind how the analysis methods and techniques work through their underlying algorithms and these are often fundamental to problem solving in the workplace. One very simple example that I have encountered numerous times is ‘why are my points appearing in the middle of the ocean?’ Because most of use say ‘lat-long’ we assume that latitude is the x-axis and longitude the y when it is the opposite. At the end of the day anyone can make a map, it’s not really the hard part of GIS, but making a good map after performing several geoprocessing tasks and performing geostatistical analysis on data and putting forward competent results is! If you are serious about a career in GIS I suggest that you attend a course that purely focuses on GIS, how and why the software does what it does and how you can use the software to solve questions, problems, and aid in better decision making. I have completed two full-time GIS focused postgraduate courses, the GIS and GISci landscape is vast and ever growing and no course can completely cover every aspect, but they are the foundation for future endeavours both educationally and professionally.

# Don’t Hide Behind a Keyboard (if possible)!

I understand that this approach only works if there are companies located locally or an acceptable commute away. If you are within an hour away from a plethora of potential employers and you’re hiding in room, in your pyjamas, applying to them through the personality of your keyboard, well I suggest that you get your head examined.

# GIS Related Certifications Are Not That Important

Tom Cruise (Jerry Maguire – if you didn’t quite get that one)

Don’t Work for the Sake of Working

# My Thoughts on Interviews

People are people, they are a fellow human beings who one day were in the position you are now. They should not be feared and you should not see an interview as a daunting experience. Society has painted the interview experience the way its is and it makes people nervous. Not getting the job is not the end of the world and you get better at interviews the more frequent you do them. I always thought I interviewed well. That was until I went for an IT role about 6 months ago and felt completely unnerved. This was simply my mind telling me this isn’t really what I wanted, my answers to questions seemed to always relate back to my GIS career and I knew quite quick that rejection was looming and I was quite happy with that. Shortly after I had an interview for a GIS role and I was back in flying form. I knew what I was talking about, what I wanted, what I could do, what I could bring to the team, my limitations, my aspirations, my goals, my salary expectations (which eventually put me out of the race because our evaluations were way off). People spend hours and even days preparing for an interview, building up the stress levels before they have even sat down with the prospective employer. You do not need to research the ins and outs of a company, you just have a look at their general activities, what industry or industries they operate in. GIS is GIS, pretty much the same or similar wherever you work or whatever industry you work in. I have worked in agriculture, oil and gas, mineral exploration, and engineering and had no clue about these industries before I started. Your GIS skills are often what’s wanted, not the industry that is looking for them.

Have some interview questions ready to ask. A couple of my favourites are; With the GIS landscape ever changing, how do you make sure that staff are kept abreast of best practices and have access to learning new methods and techniques as the industry evolves?, this indicates that you want somewhere where you can evolve and learn as you progress; You have painted this role in a glorified manner, but tell me one negative thing about working at this office location?, this will show confidence and that you are not afraid to ask difficult questions.

Be confident even if you have to fake it. It’s a horrible feeling leaving an interview knowing that nerves have cost you. Sit up straight, make eye contact, speak slow and clear as this will make you seem more relaxed and eventually you will be. The interviewer is not there to catch you out, they have your resume and can see what you have to offer, you wouldn’t have made it this far if they didn’t consider you a potential candidate for the position. Smile, smile, smile, always be smiling, you will stand out a mile from other candidates that may have interviewed better, a constant smile will work wonders.

And remember, you’re interviewing them too, you need to find out if you really want to work there so ask the right questions to get the information you need.

# Make Yourself Indispensable

When you are employed find something niche that adds to your stock. Something that if you were to disappear tomorrow they would be completely lost without you. It’s actually easier to do than you think but involves going way beyond simple GIS techniques and requires a lot of motivation. You may need to get to grips with specialist software such as FME, programming and scripting such as Python and JavaScript, learning geostatistical methods and chaining several intricate processing tasks with automation for examples but certainly not limited to these. I suggest that you find and work towards finding something that makes them fear losing you.

# Suggestions and Opinions Rant Over

If you are looking for GIS career advice make sure that you talk to GIS professionals that have been through what you are beginning to embark on and don’t rely on the generic career advice posts that paint everything in black and white. You need to tailor your do’s and do not’s to suit you as an individual and the path that you want to take. The more you talk to the more likely you will come across someone with similar experiences to you and can point you in the right direction. Avoid taking career advice from academics who like to talk about working in the ‘real world’ but have never done so.

You can use or dismiss some the suggestions above because at the end of the day they are only suggestions and as said at the beginning these are my personal experiences and my opinions.

# Book Review: The ESRI Guide to GIS Analysis Vol. 2: Spatial Measurements & Statistics

Title: The ESRI Guide to GIS Analysis Vol. 2: Spatial Measurements & Statistics
Author: Andy Mitchell
Publisher: ESRI Press
Year: 2005
Aimed at: GIS/Analysts/Map Designers – intermediate
Purchased from: www.wordery.com

This textbook acts as companion text for GIS Tutorial 2: Spatial Analysis Workbook (for ArcGIS 10.3.x) where you can match up the chapters in each book. Although not a necessity, I would recommend using both texts in tandem to apply the theory and methods discussed with practical tutorials and walkthroughs using ArcGIS. This is the second book of the series and follows on from The ESRI Guide to GIS Analysis Volume 1: Geographic Patterns & Relationships.

The first chapter is, inevitably, an introduction to spatial measurements and statistics. You perform analysis to answer questions and to answer these questions you not only need data but you also need to understand the data. Are you using nominal, ordinal, interval or ratio values, or a combination of these? The type of value(s) will shape the analysis techniques and methods used to calculate the statistics. You will need to interpret the statistics, test their significance and question the results. These elements are briefly visited with the premise of getting more in-depth as the book progresses. The chapter ends with a section on ‘Understanding data distributions’ which is essentially a brief introduction to data exploratory techniques such as describing frequency distributions, spatial distributions, and the presence of outliers and how they can affect analysis.

Chapter 2 discusses measuring geographic distributions with the bulk of the chapter focused on finding the center (mean, meridian, central feature), and measuring compactness (standard distance), orientation and direction of distributions (spatial trends). These are discussed for points, lines, and areal features and also using weighted factors based on attributes. These are useful for adding statistical confidence to patterns derived from a map. Formulas and equations begin to surface and although not necessary to learn them off by heart, because the GIS does all the heavy lifting for you, it gives insight into what goes on under the hood, and knowing the underlying theory and formulas can often aid in troubleshooting and producing accurate analysis. The last section of this chapter is fundamental to the rest of the text, testing statistical significance. This allows you to measure a confidence level for your analysis using the null hypothesis, p-value, and z-score. This can be a difficult topic to comprehend and may require further reading.

The third chapter, a lengthy one, is based around using statistical analysis to identify patterns, to enhance and backup the visual analysis of the map with confidence or to find patterns not may not have been immediately obvious. The human eye will often see patterns that do not really exist, so alternatively, statistical analysis might indicate what you thought was a strong pattern was actually quite weak. The statistical analysis methods are beginning to heat up and here we are introduced to; the Kolmorogov-Smirnov test and Chi Square test for quadrat analysis in identifying patterns in areas of equal size; the nearest neighbour index for calculating the average distance between features and identifying clustering or dispersion; and the K-function as an alternative to the nearest neighbour index, each used to measure the pattern of feature locations. These are followed by measuring the spatial pattern of feature values using; the join count statistic for areas with categories; Geary’s c and Moran’s I for measuring the similarity of nearby features, and the General-G statistic for measuring the concentration of high and low values for features having continuous values. The formulas for each are presented along with testing the significance of and interpreting the results. The final section of this chapter discusses defining spatial neighbourhoods and weights when analysing patterns. There are a few things to consider such as local or regional influences, thresholds of influence, interaction between adjacent features, and the rate of regional decline of influence.

Chapter 4 is titled ‘Identifying Clusters’ with a main focus on hotspot analysis. First, we are introduced to nearest neighbour hierarchical clustering which is heavily used in crime analysis. While Chapter 3 discussed global methods for identifying patterns and returns a single statistic, this chapter focuses on local statistics to show where these patterns exist within the global setting. Geary’s c and Moran’s I both have local versions and their definition, implementation, and factors influencing the results are discussed and critiqued along with Art Getis’ and Keith Ord’s Gi* method for identifying hot and cold spots.While the methods in Chapter 3 enforced that there are patterns in the data (or not), the methods in Chapter 4 highlight where these clustered patterns are. The last section of Chapter 4 discusses using statistics with geographic data; how the very nature of geographic data affects your analysis, how geographic data is represented in a GIS affects your data analysis, the influence of the study area boundary, and GIS data and errors.

“To the extent you’re confident in the quality of your GIS data, you can be confident in the quality of your analysis results.”

The last chapter ventures away from identifying patterns and clusters and focuses on analysing geographic relationships and using statistics to analyse such. Geographic relationships and processes are used to predict where something is likely to occur and examining why things occur where they do. Chapter 5 looks at statistical methods for identifying geographical relationships with a Pearson’s correlation coefficient and Spearman’s correlation coefficient discussed and assessed. Linear regression (ordinary least squares), and geographically weighted regression are presented as methods for analysing geographic processes. These methods warrant a full text in their own right and there is a list of further reading available at the end of the chapter.

Overall Verdict: I feel that I will be referring back to this text a lot. Having recently completed a MSc in Geocomputation I wish that this had crossed my path during the course of my studies and I would highly recommend this book to anyone venturing into spatial analysis where statistics can aid and back up the analysis. Although they are littered throughout the chapters, you really do not need to get bogged down with the formulas behind the statistical analysis techniques, the most important points is that you understand what the methods are performing, their limitations, and how to assess the results and this book really is a fantastic reference for doing just that. Knowing the theory is a huge step to being able to apply the analysis techniques confidently and derive accurate reporting of your data.

# Book Review: Learning ArcGIS Geodatabases [eBook]

Title: Learning ArcGIS Geodatabases
Author: Hussein Nasser
Publisher: Packt Publishing
Year: 2014
Aimed at: ArcGIS – beginner to advanced
Purchased from: www.packtpub.com

After using MapInfo for four years my familiarity with ArcGIS severely declined. The last time I utilised ArcGIS in employment shapefiles were predominantly used but I knew geodatabases were the way forward. If they were going to play a big part in future employment it made sense to get more intimate with them and learn their inner secrets. This compact eBook seemed like a good place to start…

The first chapter is short and sweet and delivered at a beginner’s level with nice point to point walkthroughs and screenshots to make sure you are following correctly. You are briefed on how to design, author, and edit a geodatabase. The design process involves designing the schema and specifying the field names, data types, and the geometry types for the feature class you wish to create. This logical design is then implemented as a physical schema within the file geodatabase. Finally, we add data to the geodatabase through the use of editing tools in ArcGIS and assign attribute data for each feature created. Very simple stuff so far that provides a foundation for getting set-up for the rest of the book.

The second chapter is a lot bulkier and builds upon the first. The initial task in Chapter 2 is to add new attributes to the feature classes followed by altering field properties to suit requirements. You are introduced to domains, designed to help you reduce errors while creating features and preserve data integrity, and subtypes. We are shown how to create a relationship class so we can link one feature in a spatial dataset to multiple records in a non-spatial table stored in the geodatabase as an object table. The next venture in this chapter takes a quick look at converting labels to an annotation class before ending with importing other datasets such as shapefiles, CAD files, and coverage classes and integrating them into the geodatabase as a single point of spatial reference for a project.

Chapter 3 looks at improving the rough and ready design of the geodatabase through entity-relationship modelling, which is a logical diagram of the geodatabase that shows relationships in the data. It is used to reduce the cost of future maintenance. Most of the steps from the first two chapters are revisited as we are taken through creating a geodatabase based on the new entity relationship model. The new model reduces the number of feature classes and improves efficiency through domains, subtypes and relationship classes. Besides a new train of thought on modelling a geodatabase for simplicity the only new technical feature presented in the chapter is enabling attachments in the feature class. It is important to test the design of the geodatabases through ArcGIS, testing includes adding a feature, making use of the domains and subtypes, and test the attachment capabilities to make sure that your set-up works as it should.

Chapter 4 begins with the premise of optimizing geodatabases through tuning tools. Three key optimizing features are discussed; indexing, compressing, and compacting. The simplicity of the first three chapters dwindles and we enter a more intermediate realm. For indexing, how to enable attribute indexing and spatial indexing in ArcGIS is discussed along with using indexes effectively. Many of you may have heard about database indexing before, but the concept of compression and compacting in a database may be foreign. These concepts are explored and their effective implementation explained.

The first part of the fifth chapter steps away from the GUI of ArcGIS for Desktop and ArcCatalog and switches to Python programming for geodatabase tasks. Although laden with simplicity, if you have absolutely no experience with programming or knowledge of the general concepts well then this chapter may be beyond your comprehension, but I would suggest performing the walkthroughs as it might give you an appetite for future programming endeavours. We are shown how to programmatically create a file geodatabase, add fields, delete fields, and make a copy of a feature class to another feature class. All this is achieved through Python using the arcpy module. Although aimed at highlighting the integration of programming with geodatabase creation and maintenance the author also highlights how programming and automation improves efficiency.

The second part of the chapter provides an alternative to using programming for geoprocessing automation in the form of the Model Builder. The walkthrough shows us how to use the Model Builder to build a simple model to create a file geodatabase and add a feature class to it.

The final chapter steps up a level from file geodatabases to enterprise geodatabases.

“An enterprise geodatabase is a geodatabase that is built and configured on top of a powerful relational database management system. These geodatabases are designed for multiple users operating simultaneously over a network.”

The author walks us through installing Microsoft SQL Server Express and lists some of the benefits of employing an enterprise geodatabase system. Once the installation is complete the next step is to connect to the database from a local and remote machine. Once connections are established and tested an enterprise geodatabase can be created to and its functionality utilised. You can also migrate a file geodatabase to and enterprise geodatabase. The last part of Chapter 6 shows how privileges can be used to grant users access to data that you have created or deny them access. Security is an integral part of database management.

Overall Verdict: for such a compact eBook (158 pages) it packs a decent amount of information that provides good value for money, and it also introduces other learning ventures that come part and parcel with databases in general and therefore geodatabases. Many of the sections could be expanded based on their material but the pagination would then increase into many hundreds (and more) and beyond the scope of this book. The author, Hussein Nasser, does a great job with limiting the focus to the workings of geodatabases and not veering off on any unnecessary tangents. I would recommend using complimentary material to bolster your knowledge with regards to many of the aspects such as entity-relationship diagrams, indexing (both spatial and non-spatial), Python programming, the Model Builder, enterprise geodatabases and anything else you found interesting that was only briefly touched on. Overall the text is a foundation for easing your way into geodatabase life, especially if shapefiles are still the centre of you GIS data universe.