National Urban Information System (NUIS)
Data Model
by
Rabindranath Nanda
Superintending Surveyor
GIS & RS Directorate
Survey of India
at
Indian Institute of Surveying & Mapping
Hyderabad
8th June 2010
What is NUIS ?
• The Ministry of Urban Development has launched the
National Urban Information System (NUIS) Scheme in
March, 2006. to implement 74th constitutional
ammendment.
• Components are:
• Urban Spatial Information System (USIS) to meet
the spatial requirements of urban planning.
• National Urban Databank and Indicators (NUDB&I)
to develop town level urban database.
• Developed Databases will be used for preparation of
•
Master/ Development plans.
•
Detailed town planning schemes.
•
Serve as decision support for e-governance.
Scales of GIS Data Creation
(153 Towns)
1:10000 - Preparation of Zonal/Master Plan
1:2000 - Detailed Town Planning
1:1000 - Utility Planning
Source of Data Creation
1:10000 - Thematic Mapping from imageries
1:2000 - Large Scale Mapping using 1:10K
scale aerial photographs
1:1000 - Under ground utility mapping
using Ground Penetrating Radar
National Map Policy
• National Map Policy was published during
June 2005.
• The policies announced has given rise to
two series of maps OSM and DSM.
• Mapping scale of 1:2k and 1:10k was
introduced in addition to 1:25k, 1:50k and
1:250k.
WGS 84 /
OSM Series
UTM
Everest/
Polyconic
Polyconic Series
WGS84/
LCC
DSM Series
Need to Standardized the
Data Capture
• Ensure Interoperability among different
software and hardware platforms.
• Uniformity of various datasets.
• Integrated decision support system for
different agencies could be developed.
• Automation in some stages of data
capturing/generation could be
implemented.
What is Data Modelling ?
Data modeling
• It is a method used to define and analyze data requirements
needed.
• The data requirements are recorded as a conceptual data model
with associated data definitions.
• Data modeling defines not just data elements, but their structures
and relationships between them.
• Data modeling techniques and methodologies are used to model
data in a standard, consistent, predictable manner in order to
manage it as a resource.
• Use of data modeling standards is strongly recommended for all
projects requiring a standard means of defining and analyzing data
within an organization, e.g. using data modeling:
• to manage data as a resource;
• for the integration of information systems;
• for designing databases/data warehouses.
Data Model
• The foundation to a database.
• Blue prints for data.
• Building plans for storing data.
• Instructions for building a
database.
Types of data models
Flat model
This may not strictly qualify as a data
model. The flat (or table) model
consists of a single, two-dimensional
array of data elements, where all
members of a given column are
assumed to be similar values, and all
members of a row are assumed to be
related to one another.
Hierarchical model
In this model data is organized
into a tree-like structure,
implying a single upward link in
each record to describe the
nesting, and a sort field to keep
the records in a particular order
in each same-level list.
Network model
This model organizes data
using two fundamental
constructs, called records and
sets. Records contain fields,
and sets define one-to-many
relationships between records:
one owner, many members.
Relational model
It is a database model based on
first-order predicate logic. Its core
idea is to describe a database as
a collection of predicates over a
finite set of predicate variables,
describing constraints on the
possible values and combinations
of values.
Looking Ahead
• Things to consider before
constructing or updating a data model
• Selecting a data model that best fits
your situation.
• Already have a Data Model
• Basic steps to help create and
maintain your data model
How to do ?
• Match Data to Spatial Elements
• Determine geometry type of discrete
features
• Specify relationships between features
• Implement attribute types for objects
• Select Geographic Representation
• Represent data with discreet features–
Points, Lines and Polygons
Objectives of Design
• Results in a Well-Constructed Database :
• Satisfies objectives and supports organizational
requirements
• Contains all necessary data but no redundant data
• Organizes data so that different users can access
the same data.
• Accommodates different views of the data.
• Distinguishes which applications maintain the
data. from which applications access the data.
• Appropriately represents, codes and organizes
graphical features.
Benefits From Good Design
• Data retrieval and analysis are used more
frequently.
• Decrease time in attributing data.
• Data that supports different users and
uses.
• Minimized data redundancy.
• Increased likelihood of users developing
applications.
Different Data Models in Use
• Survey of India
– Small Scale like guide maps on 1:50k/25k
– Large Scale like DDA project 1:10k and
DSSDI on 1:2k
• National Informatics Centre (NIC) project
completed by SOI on 1:1K for cities.
• NRSC Data model for 1:10k planning area
survey under NUIS project.
• National Urban Information System 1:2k
core area survey undertaken by SOI.
Basic Data Structures
• Vector Model
A vector model builds a complex representation
from primitive objects from the dimensions such
as points, lines and polygons. Examples could be
road, building, tree etc.
• Raster Model
The raster data model serves to quantize or divide
space as a series of packets or units, each of
which represents a limited, but defined, amount of
the earth’s surface. Examples could be DEM,
Ortho photos and Scanned Photographs.
Vector data model
• Location referenced by x, y coordinates,
which can be linked to form lines and
polygons.
• Attributes referenced through unique
feature ID linked to the specific row/rows of
the tables.
Raster Model
• Location is referenced by a grid cell in a
rectangular array (matrix)
• Attribute is represented as a single value
for that cell.
• More data comes in this form
– Images from remote sensing
– Scanned maps
– Elevation data
Vector Model
Salient features
• Data is to be disassociated from Visualization
and to be maintained in databases.
• Visualization is to be planned/developed for
each and every product.
• Visualization is to be again decided product wise
and developed in style sheet form.
• Where ever desired objects to be filtered and
generalized as per requirement and should be
automatic as far as practicable.
Data filtering and Generalization
1: 2000
Village Block
1: 10000
Village Block or
Oblong Hut
1: 25000
HUT
Data Creation on
different scales
Database
1:2k
Filtering/
Generalisation
Database
1:10k
Filtering/
Generalisation
Database
1:25k
Cartographic Output
Database
1:2k
Style Layer Description
for NUIS Core map
Style Layer Description
for Utility map
Database
1:10k
Style Layer Description
for Guide map
Style Layer Description
for NUIS Planning map
Style Layer Description
for Topomap
Database
1:25k
Style Layer Description
for Project map
Feature Organisation
Major Categories
1.
2.
3.
4.
5.
6.
7.
8.
Settlements & Cultural Details
Hydrography
Ocean & Coast lines
Transportation
Land Cover & Land Use
Utilities
Government/Administrative Boundaries
Land Surface Elevation: Topographic /
Hypsography
9. Geodetic
10. Vital Installations
01. Settlements & Cultural Details
•
•
•
•
•
•
•
•
Residential
Commercial
Government Offices
Religious
Antiquities
Utility Centre
Educational
Institutions/Welfare/Relief
02. Hydrography
•
•
•
•
•
•
•
•
•
Stream
Stream features
River
River Features
Lake
Lake Features
Canal
Canal features
Other Water Features
03. Ocean & Coast Lines
•
•
•
•
•
Coast Line
Coastal natural features
Coastal artificial features
Tidal
Elevation data
04. Transportation
•
•
•
•
•
•
•
•
Carriageway
Carriageway Infrastructure
Tracks
Transport Utilities
Railway
Railway Infrastructure
Railway Embankment & Cuttings
Airport
05. Land Cover & Land Use
•
•
•
•
•
•
•
•
Built up
Agricultural
Forest
West Land
Water bodies
Wet Lands
Grass Land/Grazing Land
Snow Cover
06. Utilities
•
•
•
•
•
•
•
Transmission Lines
Pipe Lines
Water Utilities
Conveyor Belt
Rope Way
Filling Stations
Solid Waste Management
07. Government & Administrative
boundaries
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
International Boundaries
State Boundaries
District Boundaries
Subdivision / Tehsil / Taluk / Mandal Boundaries
Pragana Boundaries in UP
Village Boundaries
Cantonment Boundaries
Municipal/ Corporation Boundaries
Ward Boundaries
Panchayat Boundaries
Block Boundaries
Constituency Boundaries
Police Station Boundaries
Park/Lawn Boundaries
Boundary Infrastructure
08. Land surface elevation &
Hypsography
•
•
•
•
•
Contours
Mountain Features
Mud Volcanoes
Sand Features
High Mountain Features
09. Geodetic Control Points
•
•
•
•
Primary
Secondary
Topographic
Other Sources
10.Vital Installation
• Civil Vital Installations
• Military Vital Installations
Code
01-01-00-00
Level 1
Level 2
Level 3
Level 4
2k
10k
25k
Residential
01-01-01-00
Huts
01-01-01-01
Temporary
A
P
P
01-01-01-02
Permanent
A
P
P
01-01-02-01
In ruin
A
A
p/A
01-01-02-02
Existing
A
A
P/A
01-01-03-01
Single floor
A
p/A
-
01-01-03-02
Multi Floor
A
p/A
-
01-02-01-01
Nationalized Bank
A
P
-
01-02-01-02
Private Bank
A
P
-
01-02-01-03
Chit Funds
A
P
-
01-02-02-01
Poultry Farm
A
P/A
P
01-02-02-02
Diary Farm
A
P/A
P
01-01-02-00
Village Block
01-01-03-00
01-02-00-00
01-02-01-00
01-02-02-00
Buildings
Commercial
Finance/Banks
Farms
Remarks
Geometry of the object => Feature
(NUIS)
Data Model 1:2K
• Urban Layer (Point Feature)
Table 2.1
–
–
–
–
Topographical Features
Land Marks (Religious)
Land Marks (Infrastructure)
Land Marks (Others)
• Urban Network Layer (Line Feature)
Table 2.2
–
–
–
–
Transport
Infrastructure
Topographical Features
Drainage
Data Model 1:2K
• Urban Layer (Polygon Feature)
Table 2.3
– Built-up (Residential)
– Built-up (Non-Residential)
– Religious
– Transport
– Recreational
– Administrative
– Water Bodies
– Public/Semi-public
– Other Land Uses
Data Collection
(Digital Photogrammetric)
Attribute Data Collection
(Field Work)
00030102
00030103
00030104
Attribute Data Addition
Thank You !!!