1) The document proposes developing a parametric mathematical model to improve the provision of urban services in India. 2) The model would define parameters like governance, specific services, and environment as finance functions to establish relationships between services, population, governance, and finances. 3) Sub-parameters would be derived based on preferences, costs, and inefficiencies and used to digitally represent physical and functional characteristics of services to support financial decision making.

1. DERIVING PARAMETRIC RELATIONSHIP AND
INFORMATION MODELLING OF URBAN
SERVICES FOR SUSTAINABLE GROWTH
Presented By,
Aparna Choudhary
Lekha Thakkar
Monica Priya
Nakul Chaturvedi
Rohit Singh

2. PROBLEM STATEMENT
WHAT are the inefficiencies that exist with government in the provision
of urban services ?
▫ Inefficiencies in terms of coordination within various departments.
▫ Lack of inter disciplinary vision and mechanism for future planning.
▫ Inefficiency and inadequacy in terms of finance.
▫ Lack of data base collection/maintenance/evaluation.
▫ Weak planning in allocation and distribution of funds for sustainable
development of urban services.
▫ Lack of Post Analysis of Policy Implementation on the basis of financial
impact of involved parameters.
▫ Disproportionate user prices being charged for urban services
With these existing drawbacks and rapid urbanization, how will
the government develop an approach to improvise the provision
of services systematically ??

3. CAUSES
• Over & rapid urbanisation of major cities.
• Lack of qualified manpower with technical and management skills.
• No data collection, monitoring and review system.
• Lack of technological framework for large database analysis.
• Lack of revenue sources and improper fund allocation.
• Existing framework rigid against unexpected change in demand.
• Existing manual mechanism is more inaccurate and have loopholes.
OMG!!
So
Less??

4. PROPOSAL
• To derive a parametric relationship among different urban services
and their relation with population, governance and finance using a
Mathematical Model
• All the parameters are defined as the function of finance.
Tn = f ( Gn , Sn , En )
Tn = Finance function of Total expenditure on urban services.
Gn = Finance function of Governance expenditure on urban services.
Sn = Finance function of Specific Urban service expenditure.
En = Finance function of Enivironmental expenditure on urban
services.
• Gn , Sn , En functions comprise of sub parameters.

5. Power Water
Supply
Solid Waste
Managemen
t
Transport Buildings &
Land Use
Source
Availability
Source
Availability
Infrastructure Road
Network
Building by laws
Infrastructur
e cost
Quality Treatment
Cost
Mass Transit
System
Land Values
O & M cost Accessibility O & M Cost Public
Transport
Construction
Cost on Services
Capacity
Building
Infrastructur
e
Capacity
Building
Cleaner Fuel
Cost
Slum
Rehabitation
Sustainabilit
y Cost
O & M cost Recycling Cost Infrastructure
Cost
Master Regional
Planning
Capacity
Building
Sustainability
Cost
Capacity
Building
Sustainability
Cost
Governance
Authorities
Framework
Power Allocation
Funding Distribution
Revenue Generation
Capacity Building
Policy Planning
Environment
Demography
Rainfall
Natural Resource
Availabilty
Sub Parameters

6. Sub Parameter Derivation
• Sub parameter finance function can be derived on the basis of three
variables.
Sn = f ( Pn , Cn , In )
Pn = Preference/Weightage of the sub parameter.
Cn = Capital/Cost of the sub parameter.
In = Inefficiency/Contingency of the sub parameter.
Sub parameter variables Pn ranges as 0< Pn < 1 . Like in case of Water
Supply Service, Parameter Water Source is given full preference i.e. 1
and Parameter O&M is given preference of 0.75 on the basis of
demand and service level benchmark.

7. 0
50
100
150
200
250
300
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
Average Per Capita Water
Supply
Average Per Capita
Supply
0
50
100
150
200
250
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
Average Per Capita
Expenditure
Average Per
Capita
Expenditure
Sub parameter variable Cn is calculated on per capita cost of the
service. This includes all direct costs like infrastructure etc. Below we
have taken Water Supply service for the group of urban cities and
measured the variation of resource and the expenditure on per capita
basis with time.
Sub Parameter Derivation

8. Sub parameter variables In ranges as 0< In < 1 . Inefficiency factor
includes the deviation of the total expenditure due to external factors
like leakages, force majeure etc.
So we can generate and manage the digital representations of physical
and functional characteristics of a service parameters. The
resulting service information model become shared knowledge
resource to support decision-making from financial aspect.
Sub Parameter Derivation

9. IMPLEMENTATION
Model to be incorporated in National Policy & implemented as pilot
project for specific cities.
The Stakeholders are the central and state government, private players
and finance institutions involved in providing the urban service
Infrastructure required like IT labs & equipment at central, state & local
level. Existing Setup for other national Policies can be a leverage.
Training and building of human resources for the collection,
maintenance, collaboration and evaluation of information
Writing the mathematical model in computer language and constantly
upgrading it for realistic and optimised results.
Skilled and qualified personnel to learn, manage and develop the
system.
Setup of monitoring and review mechanism for the system.

10. IMPACT
By implementing this solution, new and higher service benchmarks will
be set.
Increase in the financial efficiency and accountability.
Scalability of the Model for the rapid changes of the requirements.
Authorities well equiped and prepared for future growth predictions.
Sustainability of the solution reduces ecological impact.
Better identification and check on the inefficiencies.

11. REFERENCES
▫ Central Statistical Organisation (CSO).
▫ National Sample Survey Organisation (NSSO).
▫ Costs and Challenges of Local Urban Services.
By Kala Seetharam Sridhar & Om Prakash Mathur