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12

th Esri India User Conference 2011

FUNCTIONAL ASSESSMENT OF URBAN GREEN SPACESKshama Gupta

1, K P Sharma

1 Scientist, Indian Institute of Remote Sensing,

2 Deputy Director, Regional Centers,

3 M. Sc. Student, University of Pune, Pune

Abstract:

The study attempts to assess functionality of public green spaces in

four wards of East Delhi district of Delhi NCT, named Krishna nagar,

Jagatpuri, Preetvihar and Vishwas nagar. Total area of these wards is

17.947 Km2 Total population of this region is 35

2001. These four wards were chosen with respect to availability of

data and heterogeneous development in these wards.

Cartosat-2 and IRS P6 LISS IV merged product was used for carrying

out the mapping of urban green spaces, public gree

roads in different classes in Arc GIS v. 9.3. Eicher map of Delhi, Google

Earth and Field data used for finalize the all layer. For assessment of

functionality, buffer and network analysis was carried out in ARC GIS

and ground data for various parameters were collected

i.e., maintenance, facilities in parks, proximity to heavy traffic road,

number of uses , perception of peace, feeling of safety among users,

access to public and availability of natural vegetation. Due to time

limit, samples parks amongst total public green spaces were selected

based on their signature in remote sensing image and distribution. It

was found, from GIS analysis in Arc GIS that all parks are maintained

only in one ward i.e. Vishwas nagar ward. Authoritie

cleanliness of the park should restrict the entry of animals and anti

social elements that enhances feeling of safety among users.

Most of the sample parks provide more than two facilities as benches

and walking tracks. Other than that some parks have playing facilities

like swing, slider etc. and those parks are used by people more

frequently. 11% samples do not have any kind of facility therefore

mostly they are not in use or used by anti social elements. Big parks,

such as community and neighborhood parks should provide large

number of facilities than small parks. But during field survey it is

observed that many big parks do not provide more facilities such as

playing facilities, drinking water. Most of the sample parks people are

using for dual purpose, i.e. playing and resting. GIS is a powerful tool

for analyzing the field data especially for urban studies.

An index for assessing the functionality was developed by integrating

the GIS analysis and field parameters in ARC GIS.

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FUNCTIONAL ASSESSMENT OF URBAN GREEN SPACES- A GIS ANALYSIS, K P Sharma

1, YVN Krishnamurthy

2, Ram Kolapkar

3

Scientist, Indian Institute of Remote Sensing, ISRO/DOS, Dehradun

Deputy Director, Regional Centers, National Remote Sensing Centre, Hyderabad

M. Sc. Student, University of Pune, Pune

The study attempts to assess functionality of public green spaces in

four wards of East Delhi district of Delhi NCT, named Krishna nagar,

Jagatpuri, Preetvihar and Vishwas nagar. Total area of these wards is

17.947 Km2 Total population of this region is 358980 as per census

2001. These four wards were chosen with respect to availability of

data and heterogeneous development in these wards.

2 and IRS P6 LISS IV merged product was used for carrying

out the mapping of urban green spaces, public green spaces and

. Eicher map of Delhi, Google

Earth and Field data used for finalize the all layer. For assessment of

buffer and network analysis was carried out in ARC GIS

s parameters were collected from ground

i.e., maintenance, facilities in parks, proximity to heavy traffic road,

number of uses , perception of peace, feeling of safety among users,

access to public and availability of natural vegetation. Due to time

, samples parks amongst total public green spaces were selected

based on their signature in remote sensing image and distribution. It

that all parks are maintained

only in one ward i.e. Vishwas nagar ward. Authorities responsible for

cleanliness of the park should restrict the entry of animals and anti

social elements that enhances feeling of safety among users.

Most of the sample parks provide more than two facilities as benches

some parks have playing facilities

like swing, slider etc. and those parks are used by people more

frequently. 11% samples do not have any kind of facility therefore

mostly they are not in use or used by anti social elements. Big parks,

nd neighborhood parks should provide large

number of facilities than small parks. But during field survey it is

observed that many big parks do not provide more facilities such as

playing facilities, drinking water. Most of the sample parks people are

g for dual purpose, i.e. playing and resting. GIS is a powerful tool

for analyzing the field data especially for urban studies.

An index for assessing the functionality was developed by integrating

the GIS analysis and field parameters in ARC GIS.

About the Author:

Mrs Kshama Gupta, M.Tech

Kshama Gupta is a Scientist in

of Remote Sensing (Indian Space research

Organization) at Dehradun

Tech. in Urban Planning from School of Planning

and Architecture, New Delhi, India. Since then

she is working as researcher in the field of

remote sensing and GIS applications for urban

management.

E mail ID: [email protected]

Contact No: +91 – 9219021565

A GIS ANALYSIS

ISRO/DOS, Dehradun

National Remote Sensing Centre, Hyderabad

Tech. ( Urban Planning)

Kshama Gupta is a Scientist in Indian Institute

(Indian Space research

Organization) at Dehradun, India. She did M.

Tech. in Urban Planning from School of Planning

and Architecture, New Delhi, India. Since then

she is working as researcher in the field of

remote sensing and GIS applications for urban

[email protected],

9219021565

12


1.0 INTRODUCTION 1.1. Urban Green Spaces

“Urban green spaces are defined as public and private open spaces in urban areas, primarily covered by vegetation, which are

directly (e.g. active or passive recreation) or indirectly (e.g. positive influence on the urban environment) available for t

(Manlun, 2003).

Green spaces have their own ecological function in the environment. But when we concern with urban environment then we

find their social function as well. Urban areas can comprises large variety of green spaces, such as Parks/ gardens

green space near institution, Industrial area green spaces, P rivate Green spaces. It also includes woodlands, farm lands, et

Public gardens and play areas provide opportunity for various informal recreation and community events within se

boundaries.

Fig 1: Role of Urban Green Spaces in Urban Environment

There is very less emphasis in today’s development for leaving the open

his emotional well being within environment. There is relation between health & nature but little has been translated at the

of planning. Planning here goes by numbers i.e. this much green spa

maintenance of Public green spaces to improve the attractiveness of them to enhance the quality of urban environment.

1.2 Functionality of UGS

Functionality means a space is fulfilling the function

different functional levels. The distribution of UGS affects their functionality. They must have well distributed for satisfy

social needs of people, such as recreation, resting, playing etc. Well

that increases their functional value.

Functional levels: Empirical studies assume that green spaces fulfill different functions at different levels (Van Herzele et al.

2001). Every level in the hierarchy of UGS system has different function and different levels are complementary to each other.

Benefits of UGS

Ecological

Clean Air

Adjust Urban Climate

Water and Soil

Adjust “Urban Heat Island

Balance between

Carbon & Oxygen

Absorb toxic gases

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directly (e.g. active or passive recreation) or indirectly (e.g. positive influence on the urban environment) available for t


find their social function as well. Urban areas can comprises large variety of green spaces, such as Parks/ gardens

green space near institution, Industrial area green spaces, P rivate Green spaces. It also includes woodlands, farm lands, et

Public gardens and play areas provide opportunity for various informal recreation and community events within se

Fig 1: Role of Urban Green Spaces in Urban Environment

There is very less emphasis in today’s development for leaving the open spaces for children’s play, a place where one can restore

his emotional well being within environment. There is relation between health & nature but little has been translated at the

of planning. Planning here goes by numbers i.e. this much green space for this much population. Or planning may concern with


Functionality means a space is fulfilling the function or purpose for which it is designed. Functionality of UGS is complimentary to

different functional levels. The distribution of UGS affects their functionality. They must have well distributed for satisfy

resting, playing etc. Well-distributed green spaces can easily access by people and

Empirical studies assume that green spaces fulfill different functions at different levels (Van Herzele et al.


Benefits of UGS

Social

Eliminate Noise

Trap dust

Recreation

Landscape aesthetics

Adjust psychology

Education

Health


directly (e.g. active or passive recreation) or indirectly (e.g. positive influence on the urban environment) available for the users.”


find their social function as well. Urban areas can comprises large variety of green spaces, such as Parks/ gardens & Playgrounds,

green space near institution, Industrial area green spaces, P rivate Green spaces. It also includes woodlands, farm lands, etc.

Public gardens and play areas provide opportunity for various informal recreation and community events within settlement

spaces for children’s play, a place where one can restore

his emotional well being within environment. There is relation between health & nature but little has been translated at the level

ce for this much population. Or planning may concern with


or purpose for which it is designed. Functionality of UGS is complimentary to

different functional levels. The distribution of UGS affects their functionality. They must have well distributed for satisfying the

distributed green spaces can easily access by people and

Empirical studies assume that green spaces fulfill different functions at different levels (Van Herzele et al.


12


For example, large areas of the forest in the urban periphery may have significance to the totality of an urban area for week

recreation, while small parks in the inner city may have strong connection with the local everyday life.

Precondition for use: Distance or walking time from the home is the basic precondition for use of public green spaces. People

who live in close proximity to a green space use it frequently, rather than who live further away from green spaces.

2.0 STUDY AREA Four wards within East Delhi district of Delhi (NCT)

Latitude: 28º 37’37.38’’ N to 28º 40’03.75’’ N

Longitude: 77º 16’32.00’’ E to 77º 19’25.85’’ E

3.0 DATA USED

Table 2: Specifications of various sensors and Data used

Data

Cartosat II

IRS P6 LISS IV

Eicher map

Ward map of Delhi

Google Earth

4.0 METHOD

4.1 Thematic Data generation

The maps generated by analyzing the remote sensing data are known as the thematic maps. Urban green spaces are digitized

and classified with visual interpretation technique, using LISS

urban green spaces are as following:

Built up, Parks, Roadside green, Railway green

green, Industrial green and Water body

Using “selection by attribute” query in ARC GIS, parks and playgrounds

green spaces. Assign separate attributes to each type of park and play ground. Using

development Standards as per Master plan of Delhi for Urban Green Spaces (MPD

the parks and play grounds as public green spaces. This is secondary classification.

Table 1: Ward wise area & population distribution of the study area

Ward Name Ward No.

Krishna-nagar

Jagatpuri

Preetvihar

Vishwas-nagar

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For example, large areas of the forest in the urban periphery may have significance to the totality of an urban area for week

creation, while small parks in the inner city may have strong connection with the local everyday life.

Distance or walking time from the home is the basic precondition for use of public green spaces. People

ty to a green space use it frequently, rather than who live further away from green spaces.

Four wards within East Delhi district of Delhi (NCT)

Specifications of various sensors and Data used

Year Resolution Area

2008 1 meter East Delhi

2006 5.8 meters East Delhi

2010 1:10000 Delhi

2008 --------- Delhi

2011 1 meter Delhi


and classified with visual interpretation technique, using LISS-IV and Cartosat II merge product in ARC GIS Ver. 9.3

Railway green, Institutional green, Sports Complex, Playgrounds

arks and playgrounds were separated and exported as new layer named; public

green spaces. Assign separate attributes to each type of park and play ground. Using Table 2.5 in Chapter

Delhi for Urban Green Spaces (MPD-2021), on the basis of area of park we classify

the parks and play grounds as public green spaces. This is secondary classification.

Ward wise area & population distribution of the study area

Ward No. Population

(2001)

Area (km²)

77 70044 2.057

78 81281 1.08

79 120612 8.82

80 87043 5.99

For example, large areas of the forest in the urban periphery may have significance to the totality of an urban area for weekend

Distance or walking time from the home is the basic precondition for use of public green spaces. People

ty to a green space use it frequently, rather than who live further away from green spaces.


in ARC GIS Ver. 9.3. The classes of

Playgrounds, Vacant land, Open

as new layer named; public

Table 2.5 in Chapter–2 Hierarchy of urban

the basis of area of park we classify

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Then digitization of the road layer has been carried out. Classification of roads in different categories has been done with

help of Eicher map of Delhi. Classify the roads in following classes, Highways, Major Roads, Minor Roads, Local Roads, Railways

and Canal. Verify and correct all the classifications with help of Eicher map of Delhi and Google Earth.

4.2 Network dataset preparation

Preparation of the network dataset is very critical and essential work for network analysis.

in ARC GIS from start to end. In attribute table of digitized and classified road layer calculate the length of each type of road.

Then add one more field as Minutes that indicates travel time. Put the formula to calculate this field for local roads l*60/2

and for minor roads length *60/3600 (Here l indicates the length of road and other parameters assume the pedestrian walking

speed on local road 0.75 m / second and on minor road it is 1m / second.) For highways and major roads add the field as

restriction and put the value –1, because they are not traversable by pedestrian. These road layers are imported into one feature

dataset, which is created in personal geodatabase in Arc catalog. Within this feature dataset, create a new network dataset.

Within which assign the connectivity to local roads and minor roads at any vertex and for other roads at endpoint. Then assig

the global turns and attributes. Run and built the network dataset. Where, highways, major roads, canal and railways are

restricted and minor roads and local roads are traversable.

4.3 Buffer analysis

Buffer analysis is the vector-based proximity analysis

specified distance around the Input Features. Used following standards of buffer distance for UGS.

Table 4: Buffer distances around each category of park

Type of Park

Totlot

Housing area park

Neighborhood park

Community park

Table 3:

Totlot It is the area for

Housing area Park

and Playground

This park / playground is quiet bigger than Totlot. It has walking distance from

residents approximately 5 minutes.

Area (>5000m² to <10000m²)

Neighborhood Park

and Playground

Neighborhood Parks serve a wide range of recreational needs within the

community. Neighborhood parks / playgrounds are larger in size and have many

facilities. It has walking distance from residents

(>10000m² to <20000m²)

Community Park

and Playground

Area of natural quality, preserving unique landscaping and open space that serves

community as well as surroundings. Community parks / playgrounds are much

bigger in size and have many facilities. It has walking distance from residents

approximately 15 min

Area (>20000m² to <250000m²)

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Then digitization of the road layer has been carried out. Classification of roads in different categories has been done with

map of Delhi. Classify the roads in following classes, Highways, Major Roads, Minor Roads, Local Roads, Railways

and Canal. Verify and correct all the classifications with help of Eicher map of Delhi and Google Earth.

ration of the network dataset is very critical and essential work for network analysis. The network analysis was carried out

In attribute table of digitized and classified road layer calculate the length of each type of road.



ed on local road 0.75 m / second and on minor road it is 1m / second.) For highways and major roads add the field as

1, because they are not traversable by pedestrian. These road layers are imported into one feature

which is created in personal geodatabase in Arc catalog. Within this feature dataset, create a new network dataset.

Within which assign the connectivity to local roads and minor roads at any vertex and for other roads at endpoint. Then assig

rns and attributes. Run and built the network dataset. Where, highways, major roads, canal and railways are

nd minor roads and local roads are traversable.

based proximity analysis , which is carried out in ARC GIS ver. 9.3. It creates buffer polygons to a

specified distance around the Input Features. Used following standards of buffer distance for UGS.

Buffer distances around each category of park

Type of Park Buffer distance (m)

50

Housing area park 150

Neighborhood park 400

Community park 800

Classifications of Public green spaces

It is the area for children to play. It is located at adjacent or nearby of the house.

Area (>125m² to <5000m²)


residents approximately 5 minutes.

5000m² to <10000m²)



facilities. It has walking distance from residents approximately 10 minutes. Area

(>10000m² to <20000m²)




approximately 15 minutes.

Area (>20000m² to <250000m²)

Then digitization of the road layer has been carried out. Classification of roads in different categories has been done with the

map of Delhi. Classify the roads in following classes, Highways, Major Roads, Minor Roads, Local Roads, Railways

The network analysis was carried out

In attribute table of digitized and classified road layer calculate the length of each type of road.



ed on local road 0.75 m / second and on minor road it is 1m / second.) For highways and major roads add the field as

1, because they are not traversable by pedestrian. These road layers are imported into one feature

which is created in personal geodatabase in Arc catalog. Within this feature dataset, create a new network dataset.

Within which assign the connectivity to local roads and minor roads at any vertex and for other roads at endpoint. Then assign

rns and attributes. Run and built the network dataset. Where, highways, major roads, canal and railways are

. It creates buffer polygons to a

children to play. It is located at adjacent or nearby of the house.




approximately 10 minutes. Area




12


Put the buffer distance values in attribute table of Public green spaces layer. Then perform buffer analysis and make the ser

area polygons. Merge polygons into adjacent polygon and make only one polygon. Intersect this dissolved layer with ward

boundary of study area to get the ward wise distribution of service area. Same procedure will follow for each category of par

4.4Field Survey

4.4.1 Field data preparation:

Sampling is that part of statistical practice concerned with the selection of a subset of individual

population of individuals intended to yield some knowledge about the

making predictions based on statistical inference. For the field visit we first mark some locations on the study area as a sample

points. There are some ground truth points where we have confusion with the

basis of Remote sensing signature, Type of layouts, distribution of samples.

Remote sensing signature gives us idea about the land features

that point as ground truth point. Type of layouts is concern with type of settlements or structure of buildings.

Regarding distribution, samples should be well distributed.

whether the park is operational or not. These parameters are as follows:

4.4.2 Field parameters

Maintenance of the parks: important parameter is the maintenance if UGS is not maintained, residents cannot use it and that

decreases its functionality. Maintenance is concern with Cleanliness, Facilities, like Seating benches, Walking Tracks etc.

Facilities: analyze availability of facilities in different categories of park

etc.

Accessibility, whether park is private or public. If it is public then it can be access by any one. But if it is private then access is

limited to some people only.

Safety. By conducting survey we check the feeling of safety in the park, by interaction with people. Sometime children may

afraid to go in park due to free animals or anti social elements.

Proximity to heavy traffic road, with this parameter we check how nears the park from heavy traffic road. Sometime people may

not prefer the park, which is very close to heavy traffic road.

Natural vegetation is there or not. If more natural vegetation is available then children prefer to going i

Peace & quiet. To relax and make the mind fresh people often choose peaceful parks.

5.0 RESULTS

5.1 Ward wise distribution of Total and Public green / Person.

Calculation of total green per person and public green per person was done using following formulae.

Total green / person = Area under Total green / Total population

Public green / person = Area under Public green / Total population

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Put the buffer distance values in attribute table of Public green spaces layer. Then perform buffer analysis and make the ser

adjacent polygon and make only one polygon. Intersect this dissolved layer with ward

boundary of study area to get the ward wise distribution of service area. Same procedure will follow for each category of par

practice concerned with the selection of a subset of individual

population of individuals intended to yield some knowledge about the population of concern, especially for the purposes of

. For the field visit we first mark some locations on the study area as a sample

points. There are some ground truth points where we have confusion with the land feature. We have chosen the samples on the

basis of Remote sensing signature, Type of layouts, distribution of samples.

Remote sensing signature gives us idea about the land features - Whether it is park or not. In case of some confusion we mark


Regarding distribution, samples should be well distributed. For Primary survey we have chosen some parameters to check

whether the park is operational or not. These parameters are as follows:

: important parameter is the maintenance if UGS is not maintained, residents cannot use it and that


y of facilities in different categories of park i.e. no facility, walking tracks, benches, drinking water

. If it is public then it can be access by any one. But if it is private then access is

. By conducting survey we check the feeling of safety in the park, by interaction with people. Sometime children may

afraid to go in park due to free animals or anti social elements.

parameter we check how nears the park from heavy traffic road. Sometime people may

not prefer the park, which is very close to heavy traffic road.

is there or not. If more natural vegetation is available then children prefer to going in that parks.

. To relax and make the mind fresh people often choose peaceful parks.

5.1 Ward wise distribution of Total and Public green / Person.

Calculation of total green per person and public green per person was done using following formulae.

Total green / person = Area under Total green / Total population

Public green / person = Area under Public green / Total population

Put the buffer distance values in attribute table of Public green spaces layer. Then perform buffer analysis and make the service

adjacent polygon and make only one polygon. Intersect this dissolved layer with ward

boundary of study area to get the ward wise distribution of service area. Same procedure will follow for each category of park.

practice concerned with the selection of a subset of individual observations within a

of concern, especially for the purposes of

. For the field visit we first mark some locations on the study area as a sample

land feature. We have chosen the samples on the

it is park or not. In case of some confusion we mark


osen some parameters to check

: important parameter is the maintenance if UGS is not maintained, residents cannot use it and that


i.e. no facility, walking tracks, benches, drinking water

. If it is public then it can be access by any one. But if it is private then access is

. By conducting survey we check the feeling of safety in the park, by interaction with people. Sometime children may

parameter we check how nears the park from heavy traffic road. Sometime people may

n that parks.

12


Fig 2 : Total & Public Green Spaces / Person

The graph shows ward wise distribution of total and public green spaces per person. Krishna nagar and Jagatpuri wards have ve

less green spaces per person than other two wards i.e. less than 5 m2 / person. Although Preet Vihar and Vishwas nagar have

more than 20 m2 / person urban green spaces, but they do not have enough public green space per person as per the WHO

standards. International minimum standard suggested by World Health Organization (WHO) is a minimum availability of 9 m²

public green space per person. Hence, well planning and management should be done for the development of public green

spaces by local authority.

5.2 Network Analysis - Calculation of service areas

The complete network dataset is shown in next figure.

Fig

Table 5: Ward wise distribution of Total and Public green / Person.

Ward name Population Total green

(m²)

Krishna nagar 70044 151573.92

Jagatpuri 81281 53676.569

Preetvihar 120612 2811319.6

Vishwasnagar 87043 2460847.8

05

1015202530

Are

a u

nd

er

gre

en

m²

Urban and Public green spaces

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Total & Public Green Spaces / Person

The graph shows ward wise distribution of total and public green spaces per person. Krishna nagar and Jagatpuri wards have ve


than 20 m2 / person urban green spaces, but they do not have enough public green space per person as per the WHO


r person. Hence, well planning and management should be done for the development of public green

Calculation of service areas

The complete network dataset is shown in next figure.

3: Network Dataset Preparation

Ward wise distribution of Total and Public green / Person.

Total green

Public green

(m²)

Urban

Green / Person (m²) Green / Person (m²)

151573.92 92373.37 2.16

53676.569 26835.50 0.66

2811319.6 622337.30 23.30

2460847.8 658943.08 28.27

Wards

Urban and Public green spaces

Urban

green / Person

Public

green / Person

The graph shows ward wise distribution of total and public green spaces per person. Krishna nagar and Jagatpuri wards have very


than 20 m2 / person urban green spaces, but they do not have enough public green space per person as per the WHO


r person. Hence, well planning and management should be done for the development of public green

Public

reen / Person (m²)

1.31

0.33

5.15

7.57

12


In ArcMap, we use network analysis tool to calculate service area. Add the network dataset. Automatically it will add all the

layers with network junction points. Calculate “New service area”. In table of content Facilities, Bar

fields will appear. Add the parks point layer as facilities. Then go to the service

setting tab select impedance as minutes and put the value for default break. Here default value can change for different

facilities, such as:

These break values for time impedance decided on the basis of field observation. Then select the direction as away from the

facilities. In polygon generation tab generate polygons and trim the polygon up to 20 meters. Solve and it will automatically

calculate the area around facilities with respect to distance and time. It will create service area polygons. Export those polygons

into shape file to calculate area. Calculate the area for each category of park.

Figure 4 – Service area for different category of parks in each ward, usi

5.3 Buffer Analysis-Calculation of service areas

The graph shows ward wise distribution of service area under each category of park. In case of Krishna nagar

housing area park and community park. Therefore major service area is lying under the neighborhood park. Actually Jagatpuri

has no community and neighborhood park. But the neighborhood park, which in Preetvihar cover little service area i

Preetvihar has less percentage of service area under the category of Community Park than Neighborhood Park. Vishwas nagar

shows much realistic picture of distribution of service area. It has well distributed Totlot, Housing Area Park, Neighbor

and Community Park. Therefore service area by buffer increases respectively, i.e. less service area lying under the category

Totlot and more service area lying under the category of Community Park.

0

5

10

15

20

25

30

Krishna Nagar

Service area (%)

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layers with network junction points. Calculate “New service area”. In table of content Facilities, Barriers, Lines and polygons such

fields will appear. Add the parks point layer as facilities. Then go to the service area properties to assign parameters. In analysis


mpedance decided on the basis of field observation. Then select the direction as away from the


s with respect to distance and time. It will create service area polygons. Export those polygons

into shape file to calculate area. Calculate the area for each category of park.

Service area for different category of parks in each ward, using Network analysis

The graph shows ward wise distribution of service area under each category of park. In case of Krishna nagar


has no community and neighborhood park. But the neighborhood park, which in Preetvihar cover little service area i


shows much realistic picture of distribution of service area. It has well distributed Totlot, Housing Area Park, Neighbor

and Community Park. Therefore service area by buffer increases respectively, i.e. less service area lying under the category

Totlot and more service area lying under the category of Community Park.

Jagatpuri Preet Vihar Vishwas Nagar

Wards

Service area in % using Network Analysis


riers, Lines and polygons such

area properties to assign parameters. In analysis


mpedance decided on the basis of field observation. Then select the direction as away from the


s with respect to distance and time. It will create service area polygons. Export those polygons

ng Network analysis

The graph shows ward wise distribution of service area under each category of park. In case of Krishna nagar ward there is no


has no community and neighborhood park. But the neighborhood park, which in Preetvihar cover little service area in Jagatpuri.


shows much realistic picture of distribution of service area. It has well distributed Totlot, Housing Area Park, Neighborhood Park,

and Community Park. Therefore service area by buffer increases respectively, i.e. less service area lying under the category of

Totlot

Housing area

Neighborhood

Community

12


Fig 5 : Service area for different categ

Network analysis gives totally different picture of service areas rather than Buffer analysis. The graph shows around 25% are

under the service of Neighborhood Park in Krishna nagar ward. In Jagatpuri most

Preetvihar most of the area served by neighborhood parks (20%). Vishwasnagar gives almost same pattern like Buffer but the

percentage of area is much less than Buffer analysis. Network analysis covered about 20

simple buffer analysis.

The question may rise as why both the analysis gives different picture regarding service area of parks? The answer is buffer

analysis do not consider the accessible roads which are connected

service radius into consideration and makes the service area polygon around the parks.

roads, distance from parks, time impedance etc. also it considers restri

into account and then it calculates service area around parks. Therefore it will give more realistic and proper picture.

5.4 Field Information Analysis

5.4.1 Maintenance

Table 6 Ward wise

Ward # of samples Maintain

Jagatpuri 8

Krishna nagar 6

Preetvihar 17

Vishwas nagar 13

0

10

20

30

40

50

60

70

80

Krishna Nagar Jagatpuri

Service area (%)

Service area in % using Buffer Analysis

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: Service area for different category of parks in each ward, using Buffer analysis

Network analysis gives totally different picture of service areas rather than Buffer analysis. The graph shows around 25% are

under the service of Neighborhood Park in Krishna nagar ward. In Jagatpuri most of the area served by housing area parks. In


percentage of area is much less than Buffer analysis. Network analysis covered about 20% service area of parks determined by


analysis do not consider the accessible roads which are connected from parks to residential areas. Buffer analysis just takes

service radius into consideration and makes the service area polygon around the parks. Where network analysis considers the

roads, distance from parks, time impedance etc. also it considers restricted roads and canal as barriers. All these things are taken


Table 6 Ward wise distribution of maintenance of the samples

Maintain Not maintain % Maintained % Not Maintained

5 3 62.5 37.5

4 2 66.66 33.33

12 5 70.82 29.41

13 0 100

Jagatpuri Preet Vihar Vishwas Nagar

Wards

Service area in % using Buffer Analysis

Totlot

Housing area

Neighborhood

Community

ory of parks in each ward, using Buffer analysis

Network analysis gives totally different picture of service areas rather than Buffer analysis. The graph shows around 25% area

of the area served by housing area parks. In


% service area of parks determined by


from parks to residential areas. Buffer analysis just takes

Where network analysis considers the

cted roads and canal as barriers. All these things are taken


% Not Maintained

37.5

33.33

29.41

0

Housing area

Neighborhood

Community

12


Figure 6: Ward wise maintenance of samples

Above-mentioned graphs show maintenance of the samples. The pie chart gives us overall picture of maintenance of parks

amongst samples. In given pie chart we can easily observe almost 60% samples are well maintained and others are not.

The bar graph shows maintenance of the samples in each ward. As we can see in Vishwas nagar ward all the samples are

maintained. And in case of other wards difference

Maintenance is concern with cleanliness, well grown plants of flowers, maintained boundary with compound wall, restriction to

animals and anti social elements.

5.4.2 Facilities

Table 7 Percent

Type

No Facilities

Single Facilities (Benches / Walking Tracks)

Two Facilities (Benches & Walking tracks)

More Facilities (Benches, Walking tracks,

Benches, Walking tracks, swing, slider, Drinking Water, Toilets

Figure 8 Percentages of Facilities

0

20

40

60

80

100

120

Jagatpuri Krishna nagar Preetvihar Vishwasnagar

% Of Maintenance

Wards

Maintained Samples in each Ward

% Maintained

% Not Maintained

36%

9%

14%

of 13

Figure 7: Percentage of Maintenance

mentioned graphs show maintenance of the samples. The pie chart gives us overall picture of maintenance of parks

e chart we can easily observe almost 60% samples are well maintained and others are not.


maintained. And in case of other wards difference between maintained and not maintained samples is almost 50%.


Table 7 Percentage of types of facilities.

# of Facilities % of Facilities

No Facilities 5

Single Facilities (Benches / Walking Tracks) 13

Two Facilities (Benches & Walking tracks) 16

More Facilities (Benches, Walking tracks, swing, slider) 4

Benches, Walking tracks, swing, slider, Drinking Water, Toilets 6

Figure 8 Percentages of Facilities

% Maintained

% Not Maintained

62%

38%

% Of Maintenance

% Maintained % Not Maintained

11%

30%

No Facilities

Single Facilities (Benches / Walking Tracks)

Double Facilities (Benches & Walking tracks)

More Facilities (Benches, Walking tracks, swing, slider)

mentioned graphs show maintenance of the samples. The pie chart gives us overall picture of maintenance of parks

e chart we can easily observe almost 60% samples are well maintained and others are not.


between maintained and not maintained samples is almost 50%.


% of Facilities

11.36

29.54

36.36

9.09

13.63

More Facilities (Benches, Walking tracks, swing, slider)

12


The Pie chart explains the percentage of facilities available within all samples. Here we

two facilities (36%) i.e. benches and walking tracks. Following that single facility is available in 30% of samples. It may c

only Benches or Walking tracks. 11% samples have no facility. Other than this there

etc. In case of playing facilities swing, slider etc. are available for children.

Table 8 Facilities in different categories of parks

Facilities Totlot Housing

No 4

Single 4

Two 8

Multi 4

Figure 9 Available facilities in different categories of park

Here we do another type of analysis for facilities available in the different categories of parks. The graph shows that numbe

two facilities is more than other types of facilities. But in case of Neighborhood and Community parks we can see that number

more than two facilities parks is less, although big parks should have more facilities and multiple uses. Such as swing, slid

children, drinking water, toilets etc. In case of Community park there is one no facility park. While conducting field su

big park has been observed; this was not maintained and consists excess natural vegetation. But still people, use it for rest

Due to its large area it is lying under the category of community park.

5.4.3 Access to people and Use of Park

Table 9 “% of Accessible parks”

Access To People # of Parks % of Parks

Open to All 35 79.54

Limited 9 20.45

0

2

4

6

8

10

Totlot

# o

f F

aci

liti

es

Available facilities in different Parks

of 13

The Pie chart explains the percentage of facilities available within all samples. Here we can see that most of the samples having

two facilities (36%) i.e. benches and walking tracks. Following that single facility is available in 30% of samples. It may c

only Benches or Walking tracks. 11% samples have no facility. Other than this there are more facilities like drinking water, toilets

etc. In case of playing facilities swing, slider etc. are available for children.

Table 8 Facilities in different categories of parks

Types of Parks

Housing Neighborhood Community

0 0 1

1 8 0

3 4 1

1 3 2

Figure 9 Available facilities in different categories of park

Here we do another type of analysis for facilities available in the different categories of parks. The graph shows that numbe

two facilities is more than other types of facilities. But in case of Neighborhood and Community parks we can see that number

more than two facilities parks is less, although big parks should have more facilities and multiple uses. Such as swing, slid

children, drinking water, toilets etc. In case of Community park there is one no facility park. While conducting field su

big park has been observed; this was not maintained and consists excess natural vegetation. But still people, use it for rest

Due to its large area it is lying under the category of community park.

Table 9 “% of Accessible parks” Table 10 “% of use of Parks”

% of Parks

Housing Neighborhood Community

Type of Parks

Available facilities in different Parks

Use # of Parks

Single Use 13

>Two Use 25

Multi-Use 6

can see that most of the samples having

two facilities (36%) i.e. benches and walking tracks. Following that single facility is available in 30% of samples. It may contain

are more facilities like drinking water, toilets

Here we do another type of analysis for facilities available in the different categories of parks. The graph shows that number of

two facilities is more than other types of facilities. But in case of Neighborhood and Community parks we can see that number of

more than two facilities parks is less, although big parks should have more facilities and multiple uses. Such as swing, slider for

children, drinking water, toilets etc. In case of Community park there is one no facility park. While conducting field survey, one

big park has been observed; this was not maintained and consists excess natural vegetation. But still people, use it for resting.

Table 10 “% of use of Parks”

No

Single

Two

Multi

# of Parks % of Parks

29.54

56.81

13.63

12


Figure 10 Percentage of Accessible parks

The first pie chart shows that 80% samples are open to all people and others are accessible to limited people. In our sample

survey there are some private parks, which are located within area of residential complex. Those parks used by people, which

are living in this complex.

The second pie chart shows the use of parks. For which purpose people uses it that we observe in the f

either for playing or resting, double use indicates combination of both. And multi use means Playing, resting and socializing

Where many people can gather for recreation and enjoyment. In chart we can see more than 50% of the sample

the category of double use means they are used by people for resting and by children for playing. Very few parks are used for

socializing, because only community parks can provide such opportunity. And in our samples there is less number

parks.

5.4.5 Feeling of Safety

Table

Safety

Safe

Unsafe

Figure

80%

20%

% of Accessilbility

Open to All Limited

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Figure 11 Percentage of use of parks



The second pie chart shows the use of parks. For which purpose people uses it that we observe in the f

either for playing or resting, double use indicates combination of both. And multi use means Playing, resting and socializing

Where many people can gather for recreation and enjoyment. In chart we can see more than 50% of the sample


socializing, because only community parks can provide such opportunity. And in our samples there is less number

Table 11 Percentage of safe parks

# Of Parks % Of Parks

36 81.81

8 18.18

Figure 12 Percentage of safe parks

29%

57%

14%

% Of Use

Single Use Double Use Multi-Use

82%

18%

% of Safety

Safe

Unsafe



The second pie chart shows the use of parks. For which purpose people uses it that we observe in the field. Single use means

either for playing or resting, double use indicates combination of both. And multi use means Playing, resting and socializing.

Where many people can gather for recreation and enjoyment. In chart we can see more than 50% of the samples are lying under


socializing, because only community parks can provide such opportunity. And in our samples there is less number of community

12


Given pie chart shows percentage of samples regarding perception of

safe and in other parks they do not feel safe. Earlier we think that there may be relation between feeling of safety and nearness

of the park to the heavy traffic road, i.e. if park is near to heavy

relation between proximity to heavy traffic road and perception of feeling of safety. This case we observe in the field that

parks are near to the heavy traffic road but people do not s

park visitors and got some idea regarding their perception of feeling of safety. Feeling of safety is concern with animals an

social elements. Due to this reason children and ladie

Conclusion:

The study attempts to assess functionality of public green spaces.

from start to end using ARC GIS ver. 9.3. For that purpose mapping of urban green spaces and public green spaces has been done

on the satellite imagery. And find that Cartosat-II and LISS

areas and green spaces. Also it gives sharp boundaries of features; this was helpful to map the boundaries of green spaces, t

check the association and to decide the category of green spaces.

information. On the other hand GIS is useful to analyze the spatial data.

Ward wise analysis of urban green spaces and public green spaces gives idea about availability of parks and playgrounds.

Percentage of public parks is very less as compare to urban green spaces. Although all wards in study area have less than 9 m

person public green spaces within that Krishna nagar and Jagatpuri wards have less than 2 m

very much less area than other two wards. As per the World Health Organization (WHO) at least 9 m

be available for a person. Hence, well planning and management should be done for the development of public green spaces by

local authority.

It was observed that service area around parks using buffer and network analysis generate different values. Calculation of se

area around parks using Buffer and Network analysis gives different picture. But the most realistic one is obt

analysis; because it considers pedestrian walk distance, travel time and service area goes along the road therefore it covers

less area than Buffer analysis. Network analysis covered about 20% service area of parks determined by sim

Evaluation of field parameters gives better idea about functionality of public green spaces. Due to time limit we have chosen

samples parks amongst total public green spaces. As Krishna nagar and Jagatpuri wards have very congested

unplanned colonies, the availability of the parks is very less compare to other two wards. Regarding maintenance only in Vish

nagar ward we found our all samples are maintained. Authorities responsible for cleanliness of the park and they res

entry of animals and anti social elements that enhances feeling of safety among users. All of the private parks are well

maintained than public parks.

Most of the sample parks provide more than two facilities as benches and walking tracks. Othe

playing facilities like swing, slider etc. 11% samples do not have any kind of facility. Big parks, such as community and

neighborhood parks should provide large number of facilities than small parks. But the sample survey gives

i.e. many big parks do not provide playing facilities, drinking water. Even in neighborhood parks percentage of single facili

more than others.

Most of the sample parks people using for dual purpose, i.e. playing and resting.

parks such as community or neighborhood parks. There is relation between nearness of the parks to the heavy traffic road and

perception of peace in park. We observed in the field that those parks are situated ne

and quiet rather than those parks, which are situated away from the heavy traffic road. But there is no effect of heavy traff

roads on use of the parks by people. As park is not available nearby residential area

facilities, people bound to use those parks which are located nearby heavy traffic roads.

powerful tool to map, assimilate, integrate and doing multivariate analysis. In Th

used from start to end for creating database, generating outputs and for various GIS analysis.

of 13

Given pie chart shows percentage of samples regarding perception of feeling of safety. Around in 80% of samples people feel

Earlier we think that there may be relation between feeling of safety and nearness

of the park to the heavy traffic road, i.e. if park is near to heavy traffic road children may feel unsafe to reach there. There is no

relation between proximity to heavy traffic road and perception of feeling of safety. This case we observe in the field that

parks are near to the heavy traffic road but people do not scared to use the park. While conducting field survey we interact with

park visitors and got some idea regarding their perception of feeling of safety. Feeling of safety is concern with animals an

social elements. Due to this reason children and ladies may afraid to use the park. And hence park becomes less functional.

The study attempts to assess functionality of public green spaces. The whole mapping, analysis and final results all are generated

For that purpose mapping of urban green spaces and public green spaces has been done

II and LISS-IV merged product gives better resolution to discriminate built up

areas and green spaces. Also it gives sharp boundaries of features; this was helpful to map the boundaries of green spaces, t

check the association and to decide the category of green spaces. Remote sensing data is very useful to extract the real world

information. On the other hand GIS is useful to analyze the spatial data.


Percentage of public parks is very less as compare to urban green spaces. Although all wards in study area have less than 9 m

person public green spaces within that Krishna nagar and Jagatpuri wards have less than 2 m2

/ person public green space. This

very much less area than other two wards. As per the World Health Organization (WHO) at least 9 m2 public green space should


It was observed that service area around parks using buffer and network analysis generate different values. Calculation of se

area around parks using Buffer and Network analysis gives different picture. But the most realistic one is obt

analysis; because it considers pedestrian walk distance, travel time and service area goes along the road therefore it covers

less area than Buffer analysis. Network analysis covered about 20% service area of parks determined by sim

Evaluation of field parameters gives better idea about functionality of public green spaces. Due to time limit we have chosen

samples parks amongst total public green spaces. As Krishna nagar and Jagatpuri wards have very congested

unplanned colonies, the availability of the parks is very less compare to other two wards. Regarding maintenance only in Vish

nagar ward we found our all samples are maintained. Authorities responsible for cleanliness of the park and they res


Most of the sample parks provide more than two facilities as benches and walking tracks. Other than that some parks have


neighborhood parks should provide large number of facilities than small parks. But the sample survey gives

i.e. many big parks do not provide playing facilities, drinking water. Even in neighborhood parks percentage of single facili

Most of the sample parks people using for dual purpose, i.e. playing and resting. For socializing purpose people use only big


perception of peace in park. We observed in the field that those parks are situated near by heavy traffic road, having low peace

and quiet rather than those parks, which are situated away from the heavy traffic road. But there is no effect of heavy traff

roads on use of the parks by people. As park is not available nearby residential area or the available park may not provide good

facilities, people bound to use those parks which are located nearby heavy traffic roads. This study also concludes that GIS is a

powerful tool to map, assimilate, integrate and doing multivariate analysis. In This study ARC GIS software has been extensively

used from start to end for creating database, generating outputs and for various GIS analysis.

feeling of safety. Around in 80% of samples people feel

Earlier we think that there may be relation between feeling of safety and nearness

traffic road children may feel unsafe to reach there. There is no

relation between proximity to heavy traffic road and perception of feeling of safety. This case we observe in the field that many

cared to use the park. While conducting field survey we interact with

park visitors and got some idea regarding their perception of feeling of safety. Feeling of safety is concern with animals and anti

s may afraid to use the park. And hence park becomes less functional.

The whole mapping, analysis and final results all are generated

For that purpose mapping of urban green spaces and public green spaces has been done

erged product gives better resolution to discriminate built up

areas and green spaces. Also it gives sharp boundaries of features; this was helpful to map the boundaries of green spaces, to

Remote sensing data is very useful to extract the real world


Percentage of public parks is very less as compare to urban green spaces. Although all wards in study area have less than 9 m2

/

/ person public green space. This is

public green space should


It was observed that service area around parks using buffer and network analysis generate different values. Calculation of service

area around parks using Buffer and Network analysis gives different picture. But the most realistic one is obtained from Network

analysis; because it considers pedestrian walk distance, travel time and service area goes along the road therefore it covers much

less area than Buffer analysis. Network analysis covered about 20% service area of parks determined by simple buffer analysis.

Evaluation of field parameters gives better idea about functionality of public green spaces. Due to time limit we have chosen few

samples parks amongst total public green spaces. As Krishna nagar and Jagatpuri wards have very congested roads and

unplanned colonies, the availability of the parks is very less compare to other two wards. Regarding maintenance only in Vishwas

nagar ward we found our all samples are maintained. Authorities responsible for cleanliness of the park and they restrict the


r than that some parks have


neighborhood parks should provide large number of facilities than small parks. But the sample survey gives controversial picture

i.e. many big parks do not provide playing facilities, drinking water. Even in neighborhood parks percentage of single facility is

For socializing purpose people use only big


ar by heavy traffic road, having low peace

and quiet rather than those parks, which are situated away from the heavy traffic road. But there is no effect of heavy traffic

or the available park may not provide good

This study also concludes that GIS is a

is study ARC GIS software has been extensively

12


References:

1. Kyushik Oh et al. (2007). “Assessing the spatial distribution of urban parks using GIS”, Department of urban planning,

Hanyang University, Seoul, Republic of Korea.

2. Van Herzele et al. (2002). “A monitoring tool for the provision of accessible and attractive urba

Department Human Ecology, Free University Brussels, Belgium.

3. Dunnett et al. (2002) “Improving Urban Parks, Play Areas And Green Spaces”, Department of Landscape, University of

Sheffield Department for Transport, Local Government and the R

4. Manlun, (2003). “Suitability Analysis Of Urban Green Space System Based On GIS” International Institute for Geo

Information Science And Earth Observation Enschede, The Netherlands

5. Miller, (1996). “Urban Forestry: planning and managing urban

6. Jia J. (2001). “Planning and Design of Green Space System. Beijing, Chinese Forestry Press.

7. Comber et al. (2007), “Using a GIS based network analysis to determine urban green space accessibility for different

ethnic and religious groups.” Department of Geography, University of Leicester, UK.

8. Venn et al. (2004), “Ecology in Multi disciplinary study of urban green space: The URGE Project, Boreal Environment

Research 9: 479-489, Helsinki.

9. Master plan for Delhi, Delhi Development aut

10. Vittaya et al. (2004) “Remote sensing and GIS for Urban green space analysis, A case study of Jaipur city, Rajsthan.” ITPI

Journal 1:2 (2004) 55-67.

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Oh et al. (2007). “Assessing the spatial distribution of urban parks using GIS”, Department of urban planning,

Hanyang University, Seoul, Republic of Korea.

Van Herzele et al. (2002). “A monitoring tool for the provision of accessible and attractive urban green spaces”,

Department Human Ecology, Free University Brussels, Belgium.

Dunnett et al. (2002) “Improving Urban Parks, Play Areas And Green Spaces”, Department of Landscape, University of

Sheffield Department for Transport, Local Government and the Regions: London.

Manlun, (2003). “Suitability Analysis Of Urban Green Space System Based On GIS” International Institute for Geo

Information Science And Earth Observation Enschede, The Netherlands

Miller, (1996). “Urban Forestry: planning and managing urban green spaces.” New Jersy.

Jia J. (2001). “Planning and Design of Green Space System. Beijing, Chinese Forestry Press.

Comber et al. (2007), “Using a GIS based network analysis to determine urban green space accessibility for different

groups.” Department of Geography, University of Leicester, UK.

Venn et al. (2004), “Ecology in Multi disciplinary study of urban green space: The URGE Project, Boreal Environment

Master plan for Delhi, Delhi Development authority, (2007), “Hierarchy of Urban Development Standards, MPD

Vittaya et al. (2004) “Remote sensing and GIS for Urban green space analysis, A case study of Jaipur city, Rajsthan.” ITPI

Oh et al. (2007). “Assessing the spatial distribution of urban parks using GIS”, Department of urban planning,

n green spaces”,

Dunnett et al. (2002) “Improving Urban Parks, Play Areas And Green Spaces”, Department of Landscape, University of

Manlun, (2003). “Suitability Analysis Of Urban Green Space System Based On GIS” International Institute for Geo-

Comber et al. (2007), “Using a GIS based network analysis to determine urban green space accessibility for different

Venn et al. (2004), “Ecology in Multi disciplinary study of urban green space: The URGE Project, Boreal Environment

hority, (2007), “Hierarchy of Urban Development Standards, MPD –2021.

Vittaya et al. (2004) “Remote sensing and GIS for Urban green space analysis, A case study of Jaipur city, Rajsthan.” ITPI

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