Urban Sprawl Poster

URBAN SPRAWL
Urban growth and urban sprawl are sometimes used synonymously by the people, although they are different. Urban growth is a sum of increase in
developed land. One of its forms is expansion. Whereas urban sprawl is irregular expansion of city/town having some special characteristics (a
negative connotation).
METHODOLOGY
URBAN SPRAWL
OBJECTIVES
ANALYZE THE LAND
USE/LAND COVER
CHANGE OVER THE
LAST THREE.
STUDY THE URBAN
GROWTH PATTERN
USING SHANNON'S
ENTROPY INDEX.
PREDICT THE
URBAN GROWTH
USING THE
MODIFIED-CA
TECHNIQUE.
FORECAST THE
CHANGE IN THE
PROPERTY PRICE
IN THE YEAR 2025
To analyze the land use/land cover change over the last three
decades from multi-temporal satellite images.
To study the urban growth pattern using Shannon's entropy
index.
LITERATURE REVIEW
JOURNALS AND RESEARCHES
DATA COLLECTION
SPATIAL DATA
Accuracy
assessment
LAND USE LAND
COVER USING SUPPORT
VECTOR MACHINE
1990 2000
2010 2020
CHANGE
DETECTION
CLASSIFICATION
USING NEURAL
NETWORK
SPATIAL
ORIENTATION OF
URBAN GROWTH IN
SPIDER CHART
SHANON'S ENTROPY
MODEL SELECTION
DRIVING FACTORS
RESTRICTING
FACTORS
DELIVERABLES
LINEAR
REGRESSION
MODEL
SPATIAL
AUTOCORRELATION
Source: HCP Design, Planning and Management Pvt. Ltd
Study of urban expansion and its influence on
property price trends in Ahmedabad city and
surrounding regions
To predict the urban growth for the year 2030 using the
modified-CA technique.
To forecast the change in the property price in the year 2025 for
AMC area.
AMC database
DEM
Multispectral data
Population density
NON-SPATIAL DATA
Property Price
Change matrix
URBAN SPRAWL
PREDICTION FOR 2030
PROPERTY PRICE
FORECASTING FOR 2025
Study area
AHMEDABAD CITY PROFILE
The history of Ahmedabad can be divided into three distinct
periods, 1st The establishment during the Sultanate rule and
the pre-colonial period; 2nd, the British rule; and 3rd, the
post-colonial period.
The GHSL has been produced by the EC JRC as open and free data. These data contain a
multitemporal information layer on built-up presence as derived from Landsat image
collections (GLS1975, GLS1990, GLS2000, and ad-hoc Land sat 8 collection 2015
F o r a n a l y s i s a n d
prediction of growth in
the city and the outskirt
regions in our study
area, we took proximity
of 2.5 km from the
AMC limit and in doing
so we also avoided
influence in our study
area's growth from
n e i g h b o r i n g c i t y -
Gandhinagar. later we
created the square grid
extent from our 2.5km
buffer by minimum
bound geometry tool.
2.5 km
Latitude 23.033
Longitude 72.585
Figure 1 Pre-colonial development
The formation of city from the citadel is shown in 1st figure,
to the major industrial, commercial and residential growth in
post-colonial period is illustrated in 2nd fig below.
Study area consists of
934.32 sqkm of land
Source: Desai sowmya (2005), Urban spatial structures & land management mechanisms unpublished m. tech. planning dissertation, cept university ahmedabad
Figure 2 Development during colonial and post-colonial period
Ahmedabad is the largest city and former capital of the Indian state of Gujarat.
OBJECTIVE-1
TO ANALYZE THE LAND USE/LAND COVER CHANGE OVER THE LAST THREE DECADES FROM
MULTI-TEMPORAL SATELLITE IMAGES.
LULC 1990 LULC 2000 LULC 2010 LULC 2020
These maps are showing classification from year 1990
to 2020.
The accuracy assessment has been done with stratified
random accuracy point generation followed by
preparing confusion matrix.
LAND USE/LAND COVER CHANGE OVER THE LAST THREE DECADES
We have prepared LULC change map from 1990 to 2020 to analyze the
transition of each classes to other classes throughout these years.
Land Use Land Cover (SVM)
600.00
500.00
Area in sq.km
400.00
300.00
200.00
100.00
0.00
Built-up Agriculture / Plantation Water body Fallow land
1990 119.76 511.53 6.45 296.57
2000 199.73 354.1428 4.3875 376.0668
2010 241.056 294.5529 9.2556 389.4579
2020 310.4172 195.8418 12.9375 415.1259
Class Name
1990 2000 2010 2020
Accuracy we got for year 1990 LULC was 78.48%
followed by 2000 with 88.10% , 2010 with 84.60% and
year 2020 with 78.21% accuracy.
For built-up, the highlighted boxes are showing the
major transitions coming from other classes like
agriculture and fallow land.
The chart shows the statistics of change maps, where the areas of transitions
from 16 different classes can be known.
The change matrix has been prepared for the year 1990- 2020 to see the major transitions coming from
various classes throughout these years.
OBJECTIVE-2
TO STUDY THE URBAN GROWTH PATTERN USING SHANNON'S ENTROPY INDEX.
Classification using Neural Network - A Machine learning approach
SHANON'S ENTROPY
Shannon’s entropy describe the degree of dispersion or spatial concentration of a specific variable in a particular area.
Labelled data to train the model, this is a Supervised ML approach.
• Google’s Tensorflow library in Python to build a Neural Network (NN)
• pyrsgis — to read and write GeoTIFF
• scikit-learn — for data pre-processing and accuracy checks
• numpy — for basic array operations
B u i l d t h e m o d e l
using keras. Sequential
model to add the layers
one after the other
1990
(Bhatta(2009), Li (2004))
Shannon’s entropy (Hn) is used to measure the degree of spatial concentration or dispersion of geophysical parameter among the n zones.
The value of entropy varies from zero
to log(n). The value of zero indicates
that the distribution is very compact,
and the value near to log(n) is highly
disperse in nature. High value of
entropy indicates occurrence of
sprawl
2020
Source: Remote Sensing and Spatial Information Sciences, Volume XLII-3/W11, 2020 PECORA 21/ISRSE 38 Joint Meeting, 6–11 October 2019, Baltimore, Maryland, USA
TO PREDICT THE URBAN GROWTH FOR THE YEAR 2030 USING THE MODIFIED-CA TECHNIQUE.
LULC 2020 SIMULATION 2020 PREDICTED 2030
OBJECTIVE-3
CA model was formulated to consider all the factors which contribute to urban
growth in Study area.
The model depends primarily on the current state of the test pixel, the current state of immediate
neighboring pixels and the set of transition rules
• The simulation of year 2020
was done by considering
growth of 2000 and 2010 as a
initial years.
• The model accuracy was
85.05% with the total built-up
area of 293.73 SqKm.
Centre business district Proximity to Major Road
Amenities Quality of life Restricted zones
Assign –ve value if less than rule is required. If for a parameter, we want growth to take
place value are more than something in that case +ve value and for some parameter we
want less than something in that case value put –ve value
H e r e , H i g h
value attracts
t h e g r o w t h
t o w a r d s
features
*sqkm
• After the acceptable accuracy
from simulation result, the
prediction for the year 2030
was carried out.
• The model accuracy was
84.07% with the total built-up
area of 372..74 SqKm.
• Highly developing areas of
present time like Vaishnovdevi
and Bodakdev has shown
noticeable growth.
FORECASTING THE CHANGE IN THE PROPERTY PRICE FOR THE YEAR 2025
FORECASTED % INCREASE IN PRICE FOR 2025
OBJECTIVE-4
It is challenging to analyze or predict the property prices at
ward level as it mainly differs at micro levels, But it can be
possible to find correlation between major factors that may
affect the property prices.
Timeline Considered
• 5 years
Parameters
• Urban Growth
• Property price
.
Data Used
Very Low
• For Urban growth: Urban/built-up in 2015, 2020 and
predicted 2025 Built-up
• For Property Price: Apartment Prices in year 2015
and 2020 from the sources like: 99Acre.com,
MagicBricks.com and Makaan.com
%increase in built-up from year 2015-2020
Here, Very low value indicate below 1.72
percentage increase, Low value indicates
1.72% to 3.78%, Moderate value indicates
3.78% to 11.40%, High values indicate
11.40% to 33.24% and Rapid value indicates
more than 84.66% increase in built-up.
%increase in Price from year 2015-2020
• Almost every ward has increase in
property prices.
• Here, very low value indicate below 12
percentage increase, and Rapid value
indicates more than 118% increase in
Price.
LULC 2015 Predicted 2025
• The map shows LULC for 2015 and predicted built up for the year 2025.
• The model accuracy of 73.23 % was achieved with the built-up area of
346.18 Sqkm.
*sq km
An attempt to model the relationship between two variables by fitting a linear equation to
observed data. Where One variable is an Independent Variable, and the other is a dependent
variable.
Variables used
Independent Variable: percentage change in Built-up from 2015 to 2020 and 2020 to predicted
2025
Dependent Variable: percentage change in property prices from 2015 to 2020
Linear regression model
Scatterplot
Used to determine the
strength of the relationship
between two variables. If
there appears to be no
association between the
independent and dependent
variables, then fitting a
linear regression model to
the data probably will not
provide a useful model.
The equation for the linear
regression line is y = bx + a
From the scatterplot we got the r square value of 0.0284. which infer that there is positive
association which is not that strong among the variable.
• Highly developing
areas of present time
like ‘Chandlodia’ ,
‘Gota’ , ‘Thaltej’,
and ‘Bodakdev’ are
showing higher
increase in property
prices.
• We have made an attempt to forecast the price trend for the year 2025 with
statistical model of linear regression, having % Built-up growth as Independent
and % Increase in price as Independent Variables.
• We got the forecasted increase in the property prices by 25% to 26% for the year
2025 .
SPATIAL AUTOCORRELATION FOR BUILT-UP GROWTH AND PROPERTY PRICE
%increase in built-up from year 2015-2020
• We performed spatial autocorrelation
through global Moran's index for builtup
growth from year 2015 to 2020 to
understand correlation of Built-up over
the space.
• For percentage increase in built-up we
got,
‣ P-value = 0.25
‣ Z-score(critical value) = (-1.14)
‣ Moran’s index = (-0.14)
• Clearly indicates the spatial
autocorrelation for built-up growth is
random over the space and there is no
correlation among neighboring wards
for built-up increase.
%increase in Price from year 2015-2020
• We performed spatial autocorrelation
through global Moran's index for property
price increase from year 2015 to 2020 to
understand correlation of property price
over the space.
• For percentage increase in property price
we got,
‣ P-value = 0.079
‣ Z-score(critical value) = 1.75
‣ Moran’s index = 0.18
• Indicates the spatial autocorrelation of
property price growth has clustered
pattern over the wards with significant
correlation over the space.
CONCLUSION
CA model allows experiment to conducted on simulated system rather than the real thing. It allows the alternative scenario to be
evaluated.
CA Model had the accuracy of 85.05%. These accuracy based on different parameters has taken into consideration and pixel
transition based on input parameters.
We can consider Urban growth while forecasting the property price trends, but one might look for more numbers of independent
variables to be included in the study so that it can explain the variation in the property price more significantly.
From spatial autocorrelation we are getting spatial pattern with statistical significance like Built-up growth have random pattern
over space while property price had Clustered pattern depicting correlation over the space.