ISO 37166:2022

INTERNATIONAL ISO
STANDARD 37166
First edition
2022-02
Smart community infrastructures —
Urban data integration framework for
smart city planning (SCP)
Infrastructures urbaines intelligentes — Cadre d'intégration des
données urbaines pour la planification des villes intelligentes
Reference number
© ISO 2022
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ii
Contents Page
Foreword .v
Introduction . vi
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Principles . 3
4.1 General . 3
4.1.1 General . 3
4.1.2 Data availability . 3
4.1.3 Sovereignty over the data . 3
4.1.4 Data security . 3
4.1.5 Data privacy . . 3
4.1.6 Co-construction and sharing . 4
4.2 Principles of heterogeneous data integration . 4
4.2.1 General . 4
4.2.2 Unambiguity . 4
4.2.3 Scalability . 4
4.2.4 Compatibility . 4
4.2.5 Modularity . 4
4.3 Data quality recommendations . 4
5 Data of SCP on community infrastructure . 5
5.1 General . 5
5.2 Usage of community infrastructure data . 5
5.2.1 Construction project life cycle . 5
5.2.2 Urban simulation . 6
5.2.3 Smart transportation . 6
5.2.4 Smart grid . 6
5.2.5 Smart environmental sanitation . 6
5.3 Smart city planning (SCP) data . 6
5.4 Community infrastructure data . 11
5.4.1 General . 11
5.4.2 Data definition . 11
5.4.3 Source of heterogeneous planning data .13
6 SCP data integration framework .13
6.1 General .13
6.2 Integration subjects .13
6.3 Integration objects . 13
6.4 Integration process . 14
6.5 Integration results . 14
7 SCP data integration . .15
7.1 General . 15
7.2 Data model and description specification . 15
7.3 Data extraction and system exchange . 16
7.4 Data quality verification . 16
7.5 Data encoding or mapping specification . 16
7.6 Smart community infrastructure data entities . 16
7.7 Heterogeneous data integration . 18
7.8 Date management recommendations . 18
7.8.1 General . 18
7.8.2 Data exchange and sharing . 18
7.8.3 Data exchange and sharing security . 18
iii
8 Management of security and privacy .18
8.1 General . 18
8.2 Data security level and protection principles . 19
8.3 Technical advice for data security . 19
8.4 Life cycle safety of data . 19
8.5 System security protection .20
Annex A (informative) Case studies .21
Bibliography .27
iv
Foreword
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This document was prepared by Technical Committee ISO/TC 268, Sustainable cities and communities,
Subcommittee SC 1, Smart community infrastructures.
Any feedback or questions on this document should be directed to the user’s national standards body. A
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v
Introduction
The city is a product of social evolution, technology, economic and social civilization improvements, as
well as a fundamental unit for the social and economic life of its region. However, with the influence of
global urbanization, increasingly more problems have been observed, such as environmental pollution,
traffic congestion, insufficient resources and urban lifeline system weakness.
Urban planning refers to the conduct of engineering construction, economy, society, environment and
land use of the city and its surroundings. It involves the regional layout of industry, the regional layout
of buildings, the setting of transportation infrastructure and the planning of urban engineering. It is
related to urban development and city infrastructure construction.
The planning, construction, operation, management and evaluation of community infrastructure is
the process of natural environment transformation. This process involves multiple city managers and
various data. Therefore, the integration of heterogeneous data for smart community infrastructure
planning is particularly important. Based on ecological and spatial information, the smart city planning
(SCP) data and infrastructure data that need to be integrated should be analysed. The establishment of
a data integration framework and further realization of heterogeneous data integration is intended to
support the operation of community infrastructure construction projects throughout their life cycles
and ultimately achieve inclusive, sustainable and high-quality development of the city.
In terms of smart community infrastructure, ISO/TS 37151 describes the principles and requirements
of performance metrics. ISO/TR 37152 gives possible issues and solutions in developing and operating
smart community infrastructure, outline and benefits of a common framework for development and
operation. In addition, BS/PAS 183 provides data interoperability, types of data, data protection reform,
data value chain, purposes for data use, assessing data states, access rights for data and data structure.
ISO/TS 37151, ISO/TR 37152 and BS/PAS 183 provide the basis and guidance for ISO 37156, which
describes the types and model, opportunities, privacy and security of data exchange and sharing,
and provides guidance for data exchange and sharing of smart community infrastructure. ISO 37156
provides guidance for the integration of infrastructure data in this document, and this document is
considered to be an application scenery of ISO 37156 in data integration.
vi
INTERNATIONAL STANDARD ISO 37166:2022(E)
Smart community infrastructures — Urban data
integration framework for smart city planning (SCP)
1 Scope
This document establishes a data framework that involves possible multi-source common data through
standardized data integration and sharing mechanisms. It includes recommendations for:
— precision, dimensions of the data, data collection, updates and storing mechanisms;
— a data model for data integration, data standardization and data fusion approaches for heterogeneous
smart city infrastructure data;
— a data security level and sharable attributes for all involved data, principles on data sharing or
exchange.
This document focuses on the integration and application of heterogeneous data from urban
infrastructure systems, such as water, transport, energy, drainage and waste, so as to support smart
city planning (SCP). It contains case studies, in Annex A, of various smart city projects.
2 Normative references
There are no normative references in this document.
3 Terms and definitions
For the purposes of this document, the following terms and definitions apply.
ISO and IEC maintain terminological databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https:// www .iso .org/ obp
— IEC Electropedia: available at https:// www .electropedia .org/
3.1
data
reinterpretable representation of information in a formalized manner suitable for communication,
interpretation or processing
Note 1 to entry: Data can be processed by humans or by automatic means.
[SOURCE: ISO/IEC 2382:2015, 2121272]
3.2
data availability
property of being accessible and usable upon demand by an authorized entity
[SOURCE: ISO/IEC 27000:2018, 3.7, modified — term revised.]
3.3
data exchange
accessing, transferring, and archiving of data (3.1)
[SOURCE: ISO/TS 13399-5:2014, 3.7, modified — definition revised.]
3.4
data sharing
reference for providing shared, exchangeable and extensible data (3.1) to enable urban infrastructure
service
[SOURCE: ISO 37156:2020, 3.3.6, modified — definition revised.]
3.5
data type
defined set of data (3.1) objects of a specified data structure and a set of permissible operations, such
that these data (3.1) objects act as operands in the execution of any one of these operations
[SOURCE: ISO/IEC 20546:2019, 3.1.12, modified — Notes to entry removed.]
3.6
heterogeneous data integration
optimization method to enable effective and transformative use of data and technology from multi-
source to support sustainable development of cities and to improve the management and control of
space and resources
3.7
information
data (3.1) in context with a particular meaning
[SOURCE: ISO5127: 2017, 3.1.1.16, modified — definition revised.]
3.8
information resource
asset
record
document or item in physical or digital form that contributes to human knowledge
EXAMPLE Abstracting and indexing database.
Note 1 to entry: Information resource sometimes has a broader meaning, including information content, and also
technology resources, human resources and financial resources that enable information content management.
[SOURCE: ISO 5127:2017, 3.1.1.44; modified — definition revised and Note 2 to entry added.]
3.9
life cycle
evolution from conception through
to destruction or recycling
[SOURCE: ISO/IEC/IEEE 15288:2015, 4.1.23, modified — definition revised.]
3.10
security
condition that results from the establishment and maintenance of protective measures that ensure a
state of inviolability from hostile acts or influences
[SOURCE: IEC Guide 120:2018, 3.13]
3.11
smart community infrastructure
community infrastructure with enhanced technological performance that is designed, operated and
maintained to contributed to sustainable development and resilience of the community
[SOURCE: ISO 37156:2020, 3.1.4]
3.12
smart city planning
SCP
technical and political process concerned with the development and design of land use and the built
environment, which are enhanced by the effective and sustainable integration of informational,
physical and social systems and the transformative use of heterogeneous data and technology
3.13
system
set of interrelated or interacting functions constituted to achieve a specified objective
4 Principles
4.1 General
4.1.1 General
The data gathered and to be integrated for SCP should meet some general principles so as to ensure the
validity of the following data integration process.
4.1.2 Data availability
The data to be integrated for SCP comes from various sources, some of which are private and classified
prior to any data-sharing agreements. Thus, the description of shared data in terms of, for example,
attributes, dimensions and volumes should be available to associated integration subjects (described
in 6.2), so as to determine whether the shared data are truly available for the intended data integration
purpose.
4.1.3 Sovereignty over the data
The ownership of the source data needs to be respected during the whole data integration process
among associated stakeholders.
4.1.4 Data security
The data to be integrated for SCP should be secured during the data integration process, from data
retrieval, data clean, data storage and data output.
Regional and national security requirements such as the EU General Data Protection Regulation shall
be considered. Based on ISO/IEC 27000, considering domestic regulations and technological conditions,
an information security management systems (ISMS) or alternative necessary data security procedure
and tools should be introduced to deter possible hacker attacks and other misapplication.
The data exchange should therefore be kept to a minimum and a low level of detail. Security-relevant
data for planning, constructing, operating and managing of infrastructures should basically remain
with the data-collecting organization, ordinarily the utilities.
4.1.5 Data privacy
The data from community infrastructure to be integrated for SCP usually contains private information,
from individual socio-economic characteristics to spatial-temporal behavioural data. Integrating
and further analysing these individual-based data help to evaluate and optimize urban system
performances. But individual privacy should be respected.
The use of source data during the whole integration process should be kept on an anonymous basis. The
integration of individual data, for example consumer consumption data, should be pre-desensitized,
without personal information being exposed to either data integration engineers or terminal users.
4.1.6 Co-construction and sharing
It is possible that an intended data integration requires data from different agencies or stakeholders.
It is recommended that the necessary data are co-constructed and shared among them on a voluntary
basis. In addition, the integration results should be shared with contributors.
4.2 Principles of heterogeneous data integration
4.2.1 General
In practice, the multi-source data used for data integration varies in format, dimensions, accuracy and
durability. Although data integration approaches are evolving with more recognition or explanation
power, it is not usually encouraged to spend huge amounts on retrieving missing data. Depending on
the availability and intended use of data, it is recommended that the data integration framework applies
the following principles of heterogeneous data integration.
4.2.2 Unambiguity
The definitions and categorizations of entities should be clear where reasonably possible and available.
Categorization should be representative and mutually exclusive.
4.2.3 Scalability
Urban data integration requirements and process are continuously updating. The integrated data need
not be thoroughly completed and comprehensive in the beginning, but it is recommended that the
integration results are flexible and scalable.
4.2.4 Compatibility
Data standards applied in the data integration framework shall be compatible with existing major
urban data standards.
4.2.5 Modularity
It is recommended that data integration input, output and approaches or algorithms are defined as
modular components, so as to be used individually or in combination for different integration purposes.
In addition to unambiguity, scalability, compatibility and modularity, the urban data integration
framework should also have extensibility and interpretability. Maintaining a high level of
interpretability is vital during the integration process as the goal is to support the urban design and
operational decisions by municipal officials, policymakers and engineer technical staff. A useful urban
data integration framework should be capable of integrating heterogeneous data in an extensible (to
multiple urban systems), scalable (to the growing amounts of quickly changing urban data streams)
and interpretable manner (such that it can inform decision-making).
4.3 Data quality recommendations
On the basis of ISO 19157, the following recommendations are dependent on the intended use of
data. For example, zonal plans can be acceptable with a tolerance of several metres but plans for
individual buildings might require an absolute positional accuracy of a few centimetres. The quality
recommendation of a variety of planning data database results should ensure locational accuracy,
attribute accuracy, completeness, logical consistency, geographic quality and data relationality as far as
the data are available and it is even possible to determine such data.
a) Locational accuracy. It is recommended that the location given in the data satisfies data requirement
in terms of geographic accuracy.
b) Attribute accuracy. It is recommended that attribute values given in the data match the actual
values in the real world to the extent required by the expected use. For example, no building should
be classified as a pavement, but it can sometimes matter less if a building is classified as residential
when it is in fact partly commercial.
c) Completeness. It is recommended that the number of missing or excess data items in the data set,
in comparison to the real world, are suitable for the intended use. For example, all planning data
should contain exactly the same number of plans as actually exist, but it might not be too important
if a zonal plan contains slightly more or fewer building features.
d) Logical consistency. It is recommended that the data are logically consistent with the requirements,
in terms of concept consistency, value domain consistency and topology consistency. For example,
no plan should be described as agreed if it is only proposed; it is unlikely that a road would not have
a postal address.
e) Geographic quality. It is recommended that the geometry of the data conveys the actual data
correctly. For example, buildings are defined as polygons instead of a point when the intended
use is to correlate retail shops with a metro station spatially; the boundaries of adjacent polygon
entities coincide.
f) Data relationality. It is recommended that entities being referenced by different data sources
are mapped and related to other entities through the data model defined in the data integration
framework.
It is also noteworthy that low-quality data can still be relevant and used to identify or understand the
urban environment and phenomena that are poorly documented, but high-quality data allows for more
accurate decision-making.
5 Data of SCP on community infrastructure
5.1 General
Community infrastructure is the fundamental safeguard for residents’ lives and city development.
It incorporates various equipment and systems which are utilized for economic and social activities.
Urban infrastructure is still promoting living and economic development, but its impact on ecosystems
cannot be ignored. Technology conditions and functional loads can affect the operational efficiency
of the social and economic system and also the living quality of residents. Therefore, scientific and
reasonable urban infrastructure planning is essential to maintaining urban ecological security and
health.
The scientific infrastructure planning of smart cities requires data support, including community
infrastructure data and SCP data. SCP data are numerous and diverse and need to be reasonably
described and classified. At the same time, community infrastructure data are a core part of SCP
data. Further describing the composition, definition and source of community infrastructure data is
particularly important for the implementation of the SCP. In addition, further clarification of the usage
of infrastructure data is needed, providing support for SCP.
5.2 Usage of community infrastructure data
5.2.1 Construction project life cycle
Integrating of relevant community information resources based on the SCP data, including current
situation data, planning, data, administrative approval data and spatiotemporal big data, to realize
the entire life cycle of engineering construction projects of data exchange and sharing. Throughout
the entire life cycle of the engineering construction project, including planning, design, construction,
management and operation, the science of decision-making can be improved, and management
efficiency can be improved.
5.2.2 Urban simulation
By integrating SCP data, the distribution, scale, related activities, performance indicators and other
attributes of community infrastructure can be described and used in urban planning. In order to
understand the internal mechanism of urban system operation, city simulation can be performed after
finding out the cause of urban problems and assessing system performance.
5.2.3 Smart transportation
Infrastructure data based on transportation can support the overall control of the transportation
field and the entire process of transportation planning and management. As a result, transportation
systems have the ability to sense, interconnect, analyse, predict and control cities. In addition, it can
fully ensure traffic safety, make use of the effectiveness of transportation infrastructure, improve the
efficiency and management level of the transportation system, and serve the smooth development of
public transportation and sustainable economic development.
5.2.4 Smart grid
By integrating electricity and ICT data, real-time data collection, transmission, sharing and dynamic
monitoring can be achieved, which can enhance the scientific city and rationality of power consumption,
such as power consumption and transmission. The smart energy infrastructure has brought great
benefits to urban economic development, energy production and utilization, and environmental
protection.
5.2.5 Smart environmental sanitation
By integrating the distribution of waste transfer stations, the status of waste bins and ICT data, real-
time monitoring of environmental health services, such as infrastructure resource management, waste
collection, transportation, disposal and separation, can be achieved. This reduces the cost of sanitation
operations and supervision, improving the efficiency and quality of sanitation operations, and ensures
the cleanliness and orderliness of the urban appearance.
5.3 Smart city planning (SCP) data
This subclause presents and explains data that are useful in SCP practices. The data dimension can be
larger than community infrastructure data. The purpose is to clarify the location and connection of
community infrastructure data in SCP data.
Table 1 describes the classification and association of SCP data. As shown in Table 1, SCP data consists
of current situation data, urban planning data, administrative approval data and spatiotemporal
big data. Current situation data illustrates the current objective status of a city, including natural
environment data and built environment data (e.g. buildings, parks, bridges). Urban planning data
are thematic data on urban internal planning. Administrative approval data refers to data generated
by government administrative approval service. Spatiotemporal big data, such as traffic flow and
economic flow, is a new type of data, which is very useful for urban planning. This new type of data is
playing an increasingly important role in the planning, management and supervision of urban planning.
Current situation data are the basic data for urban planning; furthermore, urban planning data are an
important basis for administrative approval. Data generated by plan management provides guidance for
city construction, management and sustainable development. Current situation data, planning data and
administrative approval data are related to spatiotemporal big data, but related content gives priority
to the classification of spatiotemporal big data. Spatiotemporal big data are the focus of attention and
represent an important development direction of smart community infrastructure planning. Because
of their high frequency and large data volume, spatiotemporal big data require unique methods for data
integration.
Table 1 shows more details of SCP data and give examples of what the data are. These descriptions are
not exhaustive or mutually exclusive.
Table 1 — Examples of SCP data
Example of SCP data
Type of SCP data Data set category
Example data sets Source of data Examples of data insights
Current situation Infrastructure current situation data Energy data: Smart grid Location or power consump-
data tion
a
traffic data power plant location
energy data power supply
ICT data
water data
waste data
Natural environment data Natural landscape: Mapping Surface undulations
natural landscape data digital orthophoto map (DOM)
geological data
environmental data Environmental data: Sensor Air quality levels
climate data outdoor air quality
luminescence
soil data Soil data: Survey Pollution levels
Strength of soil functionality
hydrology data soil pollution
biological data, soil quality
landslide areas
Built environment data Housing estate: Survey or design Height or use
park data building dimensions data
housing estate data building occupancy
central business district
school
hospital
a
ISO 14817-1 contains information about data concepts.
b
ISO 19152 defines a reference land administration domain model (LADM) covering basic information-related components of land administration.

Table 1 (continued)
Example of SCP data
Type of SCP data Data set category
Example data sets Source of data Examples of data insights
Socioeconomic data Demographic data: Survey or census Increase or decrease
demographic data population quantity Statistical
Economic data: Survey Changes in the price level
economic data
consumer price index Crawl online
social data
Social data: Mapping Spatial extent
administrative boundaries
Planning data Infrastructure planning data Water supply planning: Water supply planning Size
traffic planning data water pipe diameter
energy planning data
ICT planning data
water planning data
waste planning data
Rural planning data Village layout planning: Village layout planning Spatial location
village layout planning village distribution
strategic planning of rural revitalization
Land use planning data Land use data: Land use planning Nature of the land
type of land use (e.g. construction land/
woodland/transportation
floor area ratio
land)/construction land use
building height intensity/height limit
a
ISO 14817-1 contains information about data concepts.
b
ISO 19152 defines a reference land administration domain model (LADM) covering basic information-related components of land administration.

Table 1 (continued)
Example of SCP data
Type of SCP data Data set category
Example data sets Source of data Examples of data insights
Specialized planning data Educational layout planning: Educational layout planning Spatial distribution pattern
river planning school size
educational layout planning school area
sports facilities layout planning school location
medical layout planning
green space planning
ecological environment planning
Administrative Infrastructure management data Traffic management data: Traffic monitoring and Traffic incident location
approval data management system
traffic management data traffic incident monitoring
data
energy management data
ICT management data
water management data Water management data: River and lake resource Development and utilization
management information management status
waste management data administrative permit of
system
wading construction projects
b
Land management data Land approval data: Planning permit management Planning and design scheme
system
land approval data land location
land supply data land price
land use management data land nature
land inspection data
Construction project management Scheme design: Approval management system Size or residential or
of construction project commercial or high or
scheme design data building scale
low or spatial location
scheme approval data building use function
engineering construction data building height
planning acceptance data building layout
a
ISO 14817-1 contains information about data concepts.
b
ISO 19152 defines a reference land administration domain model (LADM) covering basic information-related components of land administration.

Table 1 (continued)
Example of SCP data
Type of SCP data Data set category
Example data sets Source of data Examples of data insights
Real property management data Building registration data: Real estate registration system Homeowner
building registration data ownership information
forest registration data
arable land registration data
grassland registration data
Spatiotemporal Infrastructure big data: Water flow: Water sensor Water volume
big data
traffic big data real-time water flow speed
energy big data
ICT big data
water big data
waste big data
Natural environment big data Natural environment big data: Sensor Speed
real-time wind speed
Population flow Population flow: Base station equipment Commuting characteristics
phone signalling data
Economic flow Economic flow: Credit card Urban vitality
consumption data
a
ISO 14817-1 contains information about data concepts.
b
ISO 19152 defines a reference land administration domain model (LADM) covering basic information-related components of land administration.

5.4 Community infrastructure data
5.4.1 General
Smart community infrastructure includes energy, water, transportation, ICT and waste. The data
addressed in this document is related to the infrastructures and built environment elements that
support the infrastructure. Smart community infrastructure data is created, captured, collected or
curated from various sources of smart community infrastructure. Table 2 shows how the community
infrastructure data works. These descriptions are not necessarily exhaustive or mutually exclusive.
5.4.2 Data definition
Table 2 defines the data from the five community infrastructures in 5.4.1.
Table 2 — Examples of community infrastructure data
Example of infrastructure
Type of the
Data set category
infrastructure
Example data sets Source of data Examples of data insights
ICT Telecommunication connectivity Telecommunication line System logs Megabytes of data use
Signalling data Mobile communications Communication records
Internet connectivity
operator (latitude, longitude, time)
Broadband capacity, infrastructure
(gateway, transverse, radio base station),
related service and license
Energy Electrical energy Gas pipeline Flow sensor Leaks
Street lighting Smart energy meters Energy used per hour (kwh)
Thermal energy
Energy grid
Fuel gas
Waste Industrial waste Waste bin Waste bin sensor Empty or full
Transfer station Smart city management Location
Domestic waste
platform
Solid waste
Water Water supply Water pipeline network Flow sensor Leaks
Rainwater Water distribution network
Wastewater Pump station Water supply facility Water load per hour
Reclaimed water Smart water meters
Water resource
Transportation Road Public shared bicycle API for network transportation Travel path
data
Railway
Water transportation Parking lot Smart transportation Number of parking spaces
management platform
Aviation
Subway Barrier-free path Map API Distribution of barrier-free
facilities
Public transport routes Traffic lights
Public parking spaces
Non-motorized traffic
Barrier-free facility
NOTE  See ISO/TS 37151 and ISO 37156.

5.4.3 Source of heterogeneous planning data
This subclause lists typical data sources of community infrastructures and characterizations of raw
data.
City planning data may come from geographic information systems (GIS), planning and planning
approval, legal texts and planning regulations. In recent years, with the development of 3D geographic
information systems, 3D model data has been added. For example:
— data from geographic information systems;
— data from master plans and detailed plans;
— data from planning approval;
— data from 3D city model;
— data from sensors.
6 SCP data integration framework
6.1 General
This clause gives a data integration framework, including data integration subjects, objects, process
and results. Integration subjects refer to participants during data integration. Integration objects
refer to data to be integrated, which is described in Clause 5. The integration process explains the full
life cycle integration activity. The integration results refer to outputs including data, tools and rules.
The integration framework can be built on the basis of a unified standard, a unified base map, unified
planning and a unified platform.
6.2 Integration subjects
Possible subjects of heterogeneous data integration for urban infrastructure are as follows. This list is
not intended to be exhaustive.
Subject 1: Community manager, including the mayor and government industry department. Based on
heterogeneous data integration for urban infrastructure, it can change the complex and inefficient
sit
...