URBAN SPRAWL MODELING AND IT’S APPLICATION
GEOSPATIAL MODELING AND APPLICATION URBAN SPRAWL PREDICTION AND IT'S INFLUENCE ON PROPERTY PRICE TRENDS
Aseem Shaikh PG19190196
Tej Chavda PG191052
Faculty: Dr. Dipak Samal |Prof. Darshana Rawal |Mr. Ashish Upadhyay
GEOSPATIAL MODELLING & APPLICATIONS STUDIO | M2020 | SEMESTER III | MTECH GEOMATICS
URBAN SPRAWL MODELING AND IT’S APPLICATION
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).
OBJECTIVES 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.
AIM
To predict the urban growth for the year 2030 using the modified-CA technique.
Study of urban expansion and its influence on property price trends in Ahmedabad city and surrounding regions Source: HCP Design, Planning and Management Pvt. Ltd
OBJECTIVES
METHODOLOGY URBAN SPRAWL LITERATURE REVIEW JOURNALS AND RESEARCHES
To forecast the change in the property price in the year 2025 for AMC area.
Accuracy assessment
DATA COLLECTION
ANALYZE THE LAND USE/LAND COVER CHANGE OVER THE LAST THREE.
STUDY THE URBAN GROWTH PATTERN USING SHANNON'S ENTROPY INDEX.
LAND USE LAND COVER USING SUPPORT VECTOR MACHINE
CLASSIFICATION USING NEURAL NETWORK
1990
2000
2010
2020
CHANGE DETECTION
SPATIAL DATA AMC database
Multispectral data
NON-SPATIAL DATA
DEM
Population density
Property Price
SPATIAL ORIENTATION OF URBAN GROWTH IN SPIDER CHART
PREDICT THE URBAN GROWTH USING THE MODIFIED-CA TECHNIQUE.
FORECAST THE CHANGE IN THE PROPERTY PRICE IN THE YEAR 2025
MODEL SELECTION
LINEAR REGRESSION MODEL
DRIVING FACTORS RESTRICTING FACTORS
SPATIAL AUTOCORRELATION
SHANON'S ENTROPY
DELIVERABLES
Change matrix
URBAN SPRAWL PREDICTION FOR 2030
GEOSPATIAL MODELLING & APPLICATIONS STUDIO | M2020 | SEMESTER III | MTECH GEOMATICS
PROPERTY PRICE FORECASTING FOR 2025
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URBAN SPRAWL MODELING AND IT’S APPLICATION
AHMEDABAD CITY PROFILE
Study Area For analysis and 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 neighboring city-Gandhinagar. later we created the square grid extent from our 2.5km buffer by minimum bound geometry tool. Study area consists 934.32 sqkm of land
2.5 km
of
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.
Latitude 23°2' 1.9068” N Longitude 72°35' 6.0792” E
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.
Figure 2 Development during colonial and post-colonial period Source: Desai sowmya (2005), Urban spatial structures & land management mechanisms unpublished m. tech. planning dissertation, cept university ahmedabad
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GEOSPATIAL MODELLING & APPLICATIONS STUDIO | M2020 | SEMESTER III | MTECH GEOMATICS
URBAN SPRAWL MODELING AND IT’S APPLICATION
OBJECTIVE-1
TO ANALYZE THE LAND USE/LAND COVER CHANGE OVER THE LAST THREE DECADES FROM MULTI-TEMPORAL SATELLITE IMAGES.
LULC 1990
LULC 2000
Land Use Land Cover (SVM)
OPTICAL IMAGERY Year 1990 2000 2010 2020
Resolution Date of acquisition 30m Mar-90 30m Jan-00 30m Jan-10 30m Jan-20
Source USGS USGS USGS USGS
All Images have been acquired from the months of January to march only to avoid variations in some classes.
These maps are showing classification from year 1990 to 2020.
600.00 500.00
Area in sq.km
Sensor LANDSAT 5 TM LANDSAT 5 TM LANDSAT 5 TM LANDSAT 8 (OLI)
LULC 2020
LULC 2010
400.00 300.00 200.00
For the LULC classifications support vector machine algorithm was used. 100.00 The accuracy assessment has been done with stratified random accuracy point generation followed by preparing confusion matrix. 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. The chart is showing the statistics of all 3 decades of classifications. Highlighting the increasing trend of built-up and trends of other classes.
0.00 Built-up 1990 2000 2010 2020
119.76 199.73 241.056 310.4172
Agriculture / Plantation 511.53 354.1428 294.5529 195.8418
Fallow land
6.45 4.3875 9.2556 12.9375
296.57 376.0668 389.4579 415.1259
Class Name 1990
GEOSPATIAL MODELLING & APPLICATIONS STUDIO | M2020 | SEMESTER III | MTECH GEOMATICS
Water body
2000
2010
2020
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URBAN SPRAWL MODELING AND IT’S APPLICATION
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. There are total 16 classes in change map, out of which 12 classes shows only transition to other classes, the remaining 4 are retained major classes from 1990 to 2020 which are shown in red color for built up, yellow for vegetation, pink for fallow land and blue for water body. The light red shades show transition to urban from different classes, the black colors signifies the complexity and error in classification as it reflects urban to different classes. And likewise, we can see the transition in other classes.
Class Built-up Agriculture / Plantation Water body Fallow land LULC 2020
Built-up 109.3941 119.89 2.71 77.61 309.60
Agriculture / Plantation Water body 3.03 0.84 139.20 4.96 0.75 1.57 53.22 5.58 196.19 12.95
Fallow land 6.50 247.49 1.42 160.17 415.58
LULC 1990 119.76 511.53 6.45 296.57 934.32
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, where the final column of the matrix shows the initial years classes, and the last row is showing the classes at the final year after the transitions throughout the years. The colored boxes shows the persistent classes throughout the years.
For built-up, the highlighted boxes are showing the major transitions coming from other classes like agriculture and fallow land.
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GEOSPATIAL MODELLING & APPLICATIONS STUDIO | M2020 | SEMESTER III | MTECH GEOMATICS
URBAN SPRAWL MODELING AND IT’S APPLICATION
TO STUDY THE URBAN GROWTH PATTERN USING SHANNON'S ENTROPY INDEX. OBJECTIVE-2
Classification using Neural Network – A Machine learning approach
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
• Build the model using keras. sequential model to add the layers one after the other. • There is one input layer with the number of nodes equal to nBands. One hidden layer with 14 nodes and ‘relu’ as the activation function is used. The final layer contains two nodes for the binary built-up class with ‘SoftMax’ activation function, which is suitable for categorical output.
GEOSPATIAL MODELLING & APPLICATIONS STUDIO | M2020 | SEMESTER III | MTECH GEOMATICS
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URBAN SPRAWL MODELING AND IT’S APPLICATION
SPATIAL ORIENTATION OF URBAN GROWTH IN SPIDER CHART Using the spider chart we have shown the directional growth in study area by dividing the study area into 8 directional zones from the center of study area(which is very close to CBD). Major growth was seen in North east, South east , and South west directions
Direction N-NE NE-E E-SE SE-S S-SW SW-W W-NW NW-N
% Increase in Built-up 7.67 15.74 12.64 14.81 11.59 13.98 12.03 11.54
SHANON'S ENTROPY • •
Shannon’s entropy describe the degree of dispersion or spatial concentration of a specific variable in a particular area. Shannon’s entropy (Hn) is calculated to measure the degree of spatial concentration or dispersion of Built-up among 8 directional zones for the year 1990 and 2020.
(Bhatta(2009), Li (2004))
1990
2020 Zone N-NE NE-E E-SE SE-S S-SW SW-W W-NW NW-N
builtup(Xi) 15.23 25.13 23.46 23.51 6.99 9.86 8.43 15.27
Log(n)
Log(n) 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90
Total builtup Shanon's entropy 127.88 0.11 127.88 0.14 127.88 0.14 127.88 0.14 127.88 0.07 127.88 0.09 127.88 0.08 127.88 0.11 0.86
0.903089987
Zone N-NE NE-E E-SE SE-S S-SW SW-W W-NW NW-N
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 builtup(Xi) 27.18 51.96 45.42 46.96 25.66 42.68 33.87 35.88
Log(n) 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90
Total builtup Shanon's entropy 309.61 0.09 309.61 0.13 309.61 0.12 309.61 0.12 309.61 0.09 309.61 0.12 309.61 0.11 309.61 0.11 0.89
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
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GEOSPATIAL MODELLING & APPLICATIONS STUDIO | M2020 | SEMESTER III | MTECH GEOMATICS
URBAN SPRAWL MODELING AND IT’S APPLICATION
TO PREDICT THE URBAN GROWTH FOR THE YEAR 2030 USING THE MODIFIED-CA TECHNIQUE.
Cellular Automata
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
CA model kernel size define by us goes through the aery of values • kernel is at the very left cell of the raster It can't be extreme left because it doesn't have neighbors on all sides that's why it has to skip first row and first column depending upon the size of kernel • In all Other attractor and restrictor like road, CBD Quality of life , amenities ,slop it's check whether the value of the layer is greater than the threshold defined by user or note if the threshold is greater it replaced the value to 1 which is built up which means the model says that built up growth will take place
Centre business district The probability of growth will be higher and concentrated towards CBD.
Proximity to Major Road Transportation and Commuter facility will positively attract the growth.
Amenities Ward Offices, Education institutes, Hospital Zonal Offices, Libraries, Municipal Gym, Swimming Pools
Here, High value attracts the growth towards features
Quality of life Recreational spaces are the positive elements towards the quality of urban life, which had become marred by congestion due to uncontrolled growth.
Assign -ve value if less than rule is required Ex 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
GEOSPATIAL MODELLING & APPLICATIONS STUDIO | M2020 | SEMESTER III | MTECH GEOMATICS
Restricted zones These are the places where the development is restricted due to government policies and securities. These includes. Airport, Cantonment zone, Waterbodies
The model deduced the best-set of threshold value (trial & error) Obtaining the threshold value of all factors and their associated built-up pixel count value the script also monitored the contribution of each affecting factor in the generation of the new built-up pixel.
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URBAN SPRAWL MODELING AND IT’S APPLICATION BUILT-UP 2020
SIMULATION 2020
DELIVERABLE 1
*sqkm
Year 2020 2020S Built-up 310.4172 293.73 Non Built-up 623.9052 640.5924 • The simulation of year 2020 was Total area 934.3224 934.3224 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.
PREDICTED 2030
Vaishnodevi Tirthdham
*sqkm
Bodakdev
2030 372.7428 • 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.
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GEOSPATIAL MODELLING & APPLICATIONS STUDIO | M2020 | SEMESTER III | MTECH GEOMATICS
URBAN SPRAWL MODELING AND IT’S APPLICATION
TO FORECAST THE CHANGE IN THE PROPERTY PRICE FOR THE YEAR 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.
%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.
Parameters • • • .
Urban Growth Property price Timeline Considered: 5 years
Data Used • •
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 LULC 2015
%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. Very Low
Linear regression model
Predicted 2025
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
y = bx + a R² = 0.0284
• 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.
Built-up
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
2025 346.1864 *sq km
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.
GEOSPATIAL MODELLING & APPLICATIONS STUDIO | M2020 | SEMESTER III | MTECH GEOMATICS
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URBAN SPRAWL MODELING AND IT’S APPLICATION %increase %increase Builtup Builtup 15-20 15-20 %increase %increase Price Price 15-20 15-20 %increase %increase Builtup Builtup 20-25 20-25 %increase %increase Price Price 2025 2.22 2.22 12.25 12.25 22.12 22.12 25.62 25.62 2.86 2.86 23.11 23.11 16.39 16.39 25.46 25.46 24.09 24.09 21.5 21.5 16.04 16.04 25.45 25.45 1.06 1.06 20 20 1.00 1.00 25.02 25.02 5.82 5.82 9.71 9.71 3.71 3.71 25.10 25.10 33.24 33.24 118.66 118.66 5.70 5.70 25.15 25.15 1.94 1.94 20.96 20.96 0.75 0.75 25.01 25.01 3.35 3.35 53.98 53.98 2.77 2.77 25.07 25.07 84.66 84.66 21.98 21.98 25.67 25.67 25.72 25.72 22.04 22.04 4.27 4.27 16.67 16.67 25.46 25.46 5.87 5.87 27.13 27.13 28.54 28.54 25.80 25.80 0.82 0.82 26.88 26.88 3.15 3.15 25.08 25.08 1.52 1.52 32.5 32.5 16.87 16.87 25.47 25.47 2.59 2.59 22.72 22.72 5.34 5.34 25.14 25.14 5.79 5.79 7.71 7.71 4.33 4.33 25.11 25.11 1.72 1.72 6.19 6.19 3.21 3.21 25.08 25.08 24.77 24.77 32.63 32.63 2.42 2.42 25.06 25.06 2.25 2.25 9.24 9.24 3.51 3.51 25.09 25.09 3.78 3.78 28.42 28.42 18.47 18.47 25.52 25.52 29.15 29.15 25.07 25.07 37.67 37.67 26.06 26.06 5.57 5.57 22.54 22.54 10.89 10.89 25.30 25.30 11.4 11.4 6.2 6.2 8.44 8.44 25.23 25.23 5.18 5.18 18.73 18.73 5.34 5.34 25.14 25.14 0.01 0.01 34.9 34.9 18.86 18.86 25.53 25.53 0.1 0.1 26.37 26.37 14.31 14.31 25.40 25.40 2.31 2.31 24.99 24.99 8.02 8.02 25.22 25.22 0.44 0.44 33.38 33.38 39.10 39.10 26.10 26.10 1.14 1.14 19.72 19.72 0.75 0.75 25.01 25.01 1.57 1.57 14.85 14.85 4.45 4.45 25.12 25.12 0.64 0.64 31.65 31.65 21.54 21.54 25.60 25.60 19.73 19.73 36.93 36.93 22.43 22.43 25.63 25.63 3.14 3.14 39.46 39.46 25.69 25.69 25.72 25.72 1.59 1.59 42.4 42.4 38.68 38.68 26.09 26.09 1.05 1.05 37.24 37.24 37.88 37.88 26.07 26.07
FORECASTED % INCREASE IN PROPERTY PRICE FOR 2025
• 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 .
Highly developing areas of present time like ‘Chandlodia’, ‘Gota’, ‘Thaltej’ and ‘Bodakdev’ are showing higher increase in property prices.
SPATIAL AUTOCORRELATION FOR BUILT-UP GROWTH AND PROPERTY PRICE %Increase in Price (2015-2020)
%Increase in built-up (2015-2020) • We performed spatial autocorrelation through global Moran's index for built-up 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.
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• 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.
GEOSPATIAL MODELLING & APPLICATIONS STUDIO | M2020 | SEMESTER III | MTECH GEOMATICS
URBAN SPRAWL MODELING AND IT’S APPLICATION
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. DELIVERABLE 1
We got the sprawl predicted for the year 2030 with the total built-up area of 372.74 Sqkm. Where built-up increased around 62.3 Sqkm from year 2020 to 2030.
DELIVERABLE 2
We got the forecasted property price trend for the year 2025 for various AMC wards, where the increase in property price from 25% to 26% was seen as a result from the regression model.
REFERENCE ❑ Pratyush Tripathya, Amit Kumar (2019) Monitoring and modelling spatio-temporal urban growth of Delhi using Cellular Automata and geoinformatics By Pratyush Tripathya, Amit Kumara, Cities 90 (2019) 52–63 ❑ Remote Sensing and Spatial Information Sciences, Volume XLII-3/W11, 2020 PECORA 21/ISRSE 38 Joint Meeting, 6–11 October 2019, Baltimore, Maryland, USA,MANAGING URBAN SPRAWL USING REMOTE SENSING AND GIS
❑ Jain, M., Dimri, A. P., Niyogi, D., Jain, M., Dimri, A. P., & Niyogi, D. (2016). Urban sprawl patterns and processes in Delhi from 1977 to 2014 based on remote sensing and spatial metrics approaches. Earth Interactions, 20(14), 1–29. ❑ Bhatta, B. (2009). Analysis of urban growth pattern using remote sensing and GIS: A case study of Kolkata, India. International Journal of Remote Sensing, 30(18), 4733–4746 ❑ Remote Sensing and Spatial Information Sciences, Volume XLII-3/W11, 2020 PECORA 21/ISRSE 38 Joint Meeting, 6–11 October 2019, Baltimore, Maryland, USA
GEOSPATIAL MODELLING & APPLICATIONS STUDIO | M2020 | SEMESTER III | MTECH GEOMATICS
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