ISO 37114:2025

Sustainable cities and communities - Appraisal framework for datasets and data processing methods that create urban management information

This document provides: - an appraisal framework for datasets and data processing methods that create and use urban management information derived from statistics, objectives, indicators and long-term goals for sustainable development of cities and communities; - numerous combinations of data sources and data processing methods, making it easier to create and maintain urban management information and get ready for value mining of big data within cities and communities; - approaches to appraise the necessary data to generate management information in an organization and how to classify them into different categories for regular review and update over time; - functional requirements to support the design, daily operation and management of information systems. This document is designed to be compatible with artificial intelligence (AI) systems. It helps cities and communities prepare for the application of AI in digital fields towards sustainable development. This includes the adoption of AI systems to process and analyse data collected from various sources. The goal is to identify and solve problems that cities and communities face to aid decision-making and achieve the six sustainability purposes as provided in ISO 37101, which align with the UN Sustainable Development Goals (SDGs) in the long term. This document is in line with the delivery principles of a smart city provided by ISO 37106, including visionary, citizen-centric, digital, open and collaborative. This document is useful for support data and dataset management for standards on indicators for sustainable cities and communities developed by ISO/TC 268, but does not provide guidance on how to use those standards. Additionally, this document can be of use in research and educational activities.

Villes et communautés territoriales durables — Cadre d'évaluation pour les jeux de données et les méthodes de traitement des données qui génèrent des informations de gestion urbaine

General Information

• Status: Published
• Publication Date: 25-May-2025
• ICS: 13.020.20 - Environmental economics. Sustainability
• Technical Committee: ISO/TC 268 - Sustainable cities and communities
• Drafting Committee: ISO/TC 268 - Sustainable cities and communities
• Current Stage: 6060 - International Standard published
• Start Date: 26-May-2025
• Due Date: 05-Jul-2025
• Completion Date: 26-May-2025

Overview

ISO 37114:2025 - "Sustainable cities and communities - Appraisal framework for datasets and data processing methods that create urban management information" defines a structured appraisal framework to evaluate datasets and the processing methods used to produce urban management information. The standard helps cities and communities prepare, classify and maintain the data and processing chains that generate indicators, statistics and long‑term management information - with explicit compatibility for artificial intelligence (AI) systems and alignment with sustainable development goals.

Key technical topics and requirements

• Appraisal framework for datasets and processing methods: Guidance to appraise necessary data, processing chains and algorithms used to generate urban management information.
• Classification and classes: Framework organizes information into classes such as indicator, process/product specification, dataset, metadata and value appraisal, enabling regular review and updates.
• Feature catalogue and enumerations: Standard includes a feature catalogue and enumeration classes (e.g., source type, geographic metadata, measurement method, collection frequency) to standardize descriptions.
• Functional requirements for information systems: Requirements to support design, daily operation and management of systems that produce and consume urban management information.
• AI compatibility: Designed to be compatible with AI/ML processing and value mining of big data, supporting adoption of AI for data processing and analysis.
• Stakeholder engagement and context mapping: Emphasizes mapping data scenarios and engaging stakeholders across the data value chain to ensure transparency and governance.
• Support for indicators: Intended to support dataset and dataset‑processing management for ISO/TC 268 indicator standards (it does not itself prescribe how to use those indicator standards).

Practical applications and who would use this standard

ISO 37114 is practical for:

• City and regional data stewards, CIOs and smart city program managers establishing reliable urban management information.
• Urban planners and policy makers who rely on consistent indicators and documented data processes for decision‑making tied to ISO 37101 sustainability purposes and the UN SDGs.
• IT architects and system integrators designing information systems that ingest, process and expose indicator data, including AI/ML pipelines.
• Researchers and educators using the framework to teach or study data quality, metadata standards and urban data governance. Practical benefits include improved data interoperability, clearer metadata, repeatable processing chains, better readiness for AI analytics, and stronger governance for sustainable‑city indicators.

Related standards

• ISO 37101 (sustainability purposes for communities) - alignment with the six sustainability purposes.
• ISO 37106 (smart city delivery principles: visionary, citizen‑centric, digital, open and collaborative).
• ISO/TC 268 indicator standards - ISO 37114 supports dataset management for these indicators but does not replace guidance on indicator usage.

Keywords: ISO 37114, sustainable cities, urban management information, appraisal framework, datasets, data processing methods, AI compatibility, smart city, metadata, data governance, indicators.

Standards Content (Sample)

International
Standard
ISO 37114
First edition
Sustainable cities and
2025-05
communities — Appraisal
framework for datasets and data
processing methods that create
urban management information
Villes et communautés territoriales durables — Cadre
d'évaluation pour les jeux de données et les méthodes de
traitement des données qui génèrent des informations de gestion
urbaine
Reference number
© ISO 2025
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
be reproduced or utilized otherwise in any form or by any means, electronic or mechanical, including photocopying, or posting on
the internet or an intranet, without prior written permission. Permission can be requested from either ISO at the address below
or ISO’s member body in the country of the requester.
ISO copyright office
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CH-1214 Vernier, Geneva
Phone: [phone]
Email: [e-mail]
Website: www.iso.org
Published in Switzerland
ii
Contents Page
Foreword .v
Introduction .vi
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
3.1 Terms related to urban management information .2
3.2 Terms related to artificial intelligence (AI) .2
3.3 Terms related to data.3
4 Understanding the context of the appraisal framework . 4
4.1 General .4
4.2 Objectives for creating urban management information .4
4.3 Principles for establishment of appraisal framework .5
4.3.1 General .5
4.3.2 Principles related to the visionary community .5
4.3.3 Principles related to the citizen-centric community .5
4.3.4 Principles related to the digital community .5
4.3.5 Principles related to the open and collaborative community .6
4.4 Mapping the scenario of containing datasets and data processing methods .6
4.5 Engaging all interested parties along the data value generation chain .6
5 Overview of the appraisal framework . 7
6 Appraisal framework recommendations for the indicators classes . 9
6.1 General .9
6.2 Description of classes .10
6.2.1 Indicator class .10
6.2.2 Indicator classification class .10
6.2.3 Metadata record class .11
6.3 Artificial intelligence (AI) applications in indicators .11
7 Appraisal framework recommendations for the processing classes .11
7.1 General .11
7.2 Description of classes . 12
7.2.1 Process class . 12
7.2.2 Product specification class . 12
7.3 Description of enumeration class . 13
7.4 Artificial intelligence (AI) applications in dataset processing . 13
8 Appraisal framework recommendations for the datasets classes .13
8.1 General . 13
8.2 Description of classes .14
8.2.1 Dataset class .14
8.2.2 Metadata record class . 15
8.2.3 Value appraisal class . 15
8.3 Description of enumeration classes . 15
8.3.1 Source type class . 15
8.3.2 Geographic metadata class .16
8.3.3 Measurement method type class .17
8.3.4 Frequency of data collection class .17
8.3.5 Other classification class .18
8.4 Artificial intelligence (AI) applications in datasets .18
9 Feature catalogue .18
9.1 General .18
9.2 Indicator class .18
9.3 Indicator classification class . 20

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9.4 Indicator classification class: Purpose type .21
9.5 Indicator classification class: Category type . 22
9.6 Indicator classification class: Topic type . 23
9.7 Metadata record class .24
9.8 Process class . 25
9.9 Product specification class .27
9.10 Status type class. 28
9.11 Dataset class . 28
9.12 Value appraisal class . 30
9.13 Source type class .32
9.14 Geographic metadata class . 33
9.15 Measurement method type class . 33
9.16 Frequency of data collection class . 34
9.17 Other classification class . 35
Annex A (informative) An example for implementation of the appraisal framework .37
Annex B (informative) Detailed overall figure .39
Bibliography . 41

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Foreword
ISO (the International Organization for Standardization) is a worldwide federation of national standards
bodies (ISO member bodies). The work of preparing International Standards is normally carried out through
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The procedures used to develop this document and those intended for its further maintenance are described
in the ISO/IEC Directives, Part 1. In particular, the different approval criteria needed for the different types
of ISO document should be noted. This document was drafted in accordance with the editorial rules of the
ISO/IEC Directives, Part 2 (see www.iso.org/directives).
ISO draws attention to the possibility that the implementation of this document may involve the use of (a)
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rights in respect thereof. As of the date of publication of this document, ISO had not received notice of (a)
patent(s) which may be required to implement this document. However, implementers are cautioned that
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Any trade name used in this document is information given for the convenience of users and does not
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related to conformity assessment, as well as information about ISO's adherence to the World Trade
Organization (WTO) principles in the Technical Barriers to Trade (TBT), see www.iso.org/iso/foreword.html.
This document was prepared by Technical Committee ISO/TC 268, Sustainable cities and communities.
Any feedback or questions on this document should be directed to the user’s national standards body. A
complete listing of these bodies can be found at www.iso.org/members.html.

v
Introduction
Sustainable cities and communities rely on management information to support their decision-making
on cross-sectoral issues. Urban management information can be created with data obtained from various
sources – such as statistics, surveys, measurements, observations, data sharing based on API, and data
aggregation through machine learning (ML), where raw data is collected and processed – and expressed in a
summarized form for statistical analysis.
Sustainable cities and communities can be efficient by understanding:
— the value of data in improving city governance;
— the rationale and purpose of urban management information and its intended use;
— the algorithms and processing methods used for creating urban management information;
— the raw data obtained from multiple sources that combine to create urban management information.
The complexity of these four topics is increasing due to the growing demand for standardized urban
management information and the continuous generation of diverse data. As a result, organizations need
a common appraisal framework to support decision-making, without requiring deep knowledge of data
techniques and to enable effective dialogue with stakeholders.
The need for urban management information in sustainable development is clearly shown through the
creation of indicators. These indicators aggregate and process data, serving as a key metric for evaluating the
performance of sustainable cities and communities. They support both policy making and communication.
However, the use of indicators is limited by the availability of data, data processing methods, and the lack of
standardized metadata. For example:
— the same indicator can have different results using apparently similar data: datasets with the same
parameters can produce different indicator results due to different measurement or observation
methods used to create the data;
— comparable indicators can be created from different data: equivalent datasets are not available at all
locations, but by using suitable processing techniques, comparable indicators can be created;
— the suitability of a given indicator can vary from time to time: the impact can be variable purposes,
public policies, data-collection methods, data processing methods and scientific advancements;
— lack of metadata standards can also hamper collaboration and information sharing among analysts: a
city manager usually gathers data from various sources, including documents, open data portals, smart
city systems, statistics, or surveys. All the data is collected in different formats by different organizations
or individuals. Without a unified metadata standard, this data can result in duplicates, inaccuracies,
undetected bad data, or incomplete conclusions.
Organizations need to have a shared understanding of how indicators are created or selected. This helps
them use indicators effectively when making decisions. It also ensures flexibility and agility in cities or
communities by applying the right indicators at the right time. Meanwhile, the growing variety of data
sources and processing methods adds complexity to the process.
To address the complexity outlined above, organizations can adopt a consistent and structural approach to
manage the urban management information they use. This approach should be sufficiently agile and flexible
to integrate the strategic, operational, scientific and technology viewpoints that all impact how urban
management information is created and used.
As elaborated in Figure 1, the integration of strategic, operational, technical and scientific viewpoints is a
prerequisite for creating urban management information. At the strategic level, organizations need to set
sustainability aims and plan to monitor the sustainability process. At the operational level, organizations
need to define roles and responsibilities, and develop data supply agreements with stakeholders. At the
technical level, cities and communities need to have data processing technology and data storage, and
stakeholders need to communicate with each other. At the scientific level, organizations and stakeholders

vi
should have scientific understanding and promote its practical implementation as well environmental
observation. After these four aspects are well grounded, the involvement of users should be considered,
with the smart city system principles defined in ISO 37106 serving as a guide for the appraisal framework.
Then, the appraisal framework for datasets and data processing methods that create urban-management
information can be established and used.
Figure 1 — Management information conceptual model
This document sets out a conceptual model that allows different ways of implementation with spreadsheets
and databases, installed or web-based. This document facilitates communication among various stakeholders
including top management, data ownership holders, data possession rights holders, data management rights
holders, property owners, facilities management (including asset management), statistical agencies, and
others. The methodology is also technologically neutral, allowing organizations to adopt different technical
solutions for generating and using data in accordance with the UN SDGs.
This document also provides a reference for various other interested parties, including building owners,
facilities companies, statistical agencies, smart city service providers, software vendors and even financial
institutions that offer financing services for the development of the data services industry. This support
encourages their participation in the sustainable management of cities and communities. It can support
global comparison for smart and sustainable development, and is an extension to other International
Standards developed by ISO/TC 268, such as ISO 37101 and ISO 37104. Accordingly, the data in this document
is organized in response to the six purposes of a sustainable city or community elaborated in ISO 37101.

vii
Figure 2 — Big data value generation chain in smart city systems
This document aims to assist the authorities of cities and communities in gathering data from various
sources. Data processing is important for organizations and enterprises as it promotes raw data to
valuable big data assets. This process involves different interested parties including: the data originators,
who generates the raw data through their activities; the data providers who collect these raw data; and
the data holders who store and clean the data to make it usable for different scenarios and applications.
Additionally, data processors perform data mining to transform the data into indicators that can be used for
comparison. Data processors also play an important role in protecting the privacy and security of cities and
communities throughout the data mining process to develop indicators, rather than directly submitting raw
or basic data. Users can obtain these indicators from data marketplaces or open data portals. Sometimes,
data brokers can help users by introducing suitable marketplaces for data processors or data holders, or by
assisting users in purchasing complex datasets. By utilizing these indicators, valuable information can be
extracted for business and political decision-making. The indicators can reflect trends, development levels,
and relationships within different activities in cities and communities. All the data, datasets, and indicators
can be considered as part of urban information management, and they serve as important big data assets
in a smart city system. This urban management information can facilitate data sharing in sustainable
communities. The benefits of big data assets are elaborated through this appraisal framework, as shown in
Figure 2.
This appraisal framework therefore supports:
— comparison of performance of different cities and communities with different data-collection regimes;
— appraisal of data collection and data management activities in relation to urban management;
— continuous feedback and transparency of progress towards objectives and goals of city sustainable
development;
— communication between organizations and departments responsible for data collection, processing and
management activities, based on a series of common vocabularies;
— indicator management and analysis activities compatible with artificial intelligence (AI);

viii
— allowing data mining to create commercial value and to add value to open data in cities and communities;
— comparison of sustainable performance across different periods.
While this appraisal framework primarily focuses on data and dataset management, users should be aware
of the importance of protecting privacy, ensuring data security, and evaluating the potential harm of data
usage on cities and communities.
The appraisal framework is designed for urban management information, which supports the evaluation
of sustainable management systems of cities and communities. ISO 37106 describes smart processes and
operating models for sustainable communities, with an emphasis on implementation of a principles-based
smart city operating model. The appraisal framework supports this smart operating model with an emphasis
on indicators, processing and datasets. This document includes data-specific guidance to implement the
overarching strategy elaborated by ISO 37106, in which development of a smart city is informed by four
delivery principles: visionary, citizen-centric, digital and open and collaborative.
By implementing this document, data can be collected, monitored, and managed in order to facilitate
the processing of indicators in related standards such as ISO 37120, ISO 37122 and ISO 37123, as well as
other International Standards, in ways that implement the four ISO 37106 delivery principles. Through the
use of urban management information, the appraisal framework supports the implementation of the six
sustainability purposes of ISO 37101 and the seventeen UN Sustainable Development Goals (SDGs).
This document references methodologies developed by ISO/TC 211 for the creation of data product
specifications (see ISO 19131), observation and measurements (see ISO 19156) and dataset metadata (see
ISO 19115-1).
ISO 37105 uses a human anatomy analogy to describe cities through three fundamental systems: structure
(physical structures), society (urban living entities) and interactions (interactions between social and
physical structures). It aims to enhance data interoperability across urban sectors. This document provides
an appraisal framework for sustainable development management in cities and communities, mapping out
its use in conjunction with ISO 37105.
ISO 37156 focuses on the exchange and sharing of urban infrastructure data between organizations, based
on principles for data exchange concerning the technical aspects of smart community infrastructure. This
document is concerned with urban management information that has a specific significance within the
management system process. It utilizes various indicators, such as social, economic, and environmental
information, for urban data management, processing indicators, and completing the big data value chain.
It also establishes an appraisal framework for metadata specification, processes, and indicator generation.
This document is not a technical specification for data systems or infrastructures that can operate
independently of information technology (IT). It is also technology-neutral if the smart city system has
not yet been established in a city or community. The related International Standards developed by ISO,
particularly by ISO/TC 268 on smart community infrastructures, and the IEC are considered as IT technical
standards that specify the data outlined in this document.
This document is structured in the following way:
— Clause 1 describes the scope;
— Clause 2 lists normative references;
— Clause 3 sets out the terms and definitions used in this document;
— Clause 4 illustrates the understanding the context of the appraisal framework;
— Clause 5 provides an overview of the appraisal framework;
— Clause 6 describes the appraisal framework recommendations for the indicators classes;
— Clause 7 describes the appraisal framework recommendations for the processing classes;
— Clause 8 describes the appraisal framework recommendations for the datasets classes;

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— Clause 9 describes the feature catalogue of the appraisal framework;
— Annex A provides an example of implementation of the appraisal framework;
— Annex B provides further details for Figure 4.

x
International Standard ISO 37114:2025(en)
Sustainable cities and communities — Appraisal framework
for datasets and data processing methods that create urban
management information
1 Scope
This document provides:
— an appraisal framework for datasets and data processing methods that create and use urban management
information derived from statistics, objectives, indicators and long-term goals for sustainable
development of cities and communities;
— numerous combinations of data sources and data processing methods, making it easier to create and
maintain urban management information and get ready for value mining of big data within cities and
communities;
— approaches to appraise the necessary data to generate management information in an organization and
how to classify them into different categories for regular review and update over time;
— functional requirements to support the design, daily operation and management of information systems.
This document is designed to be compatible with artificial intelligence (AI) systems. It helps cities and
communities prepare for the application of AI in digital fields towards sustainable development. This
includes the adoption of AI systems to process and analyse data collected from various sources. The goal is
to identify and solve problems that cities and communities face to aid decision-making and achieve the six
sustainability purposes as provided in ISO 37101, which align with the UN Sustainable Development Goals
(SDGs) in the long term.
This document is in line with the delivery principles of a smart city provided by ISO 37106, including
visionary, citizen-centric, digital, open and collaborative. This document is useful for support data and
dataset management for standards on indicators for sustainable cities and communities developed by
ISO/TC 268, but does not provide guidance on how to use those standards. Additionally, this document can
be of use in research and educational activities.
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 terminology 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 Terms related to urban management information
3.1.1
management information
data, reports and analyses that are used by managers to make informed decisions within an organization
Note 1 to entry: This information is typically derived from various sources within the organization and is presented in
a format that is relevant and useful for managerial decision-making.
3.1.2
urban management information
data, reports and analyses used by urban planners and managers to make informed decisions related to the
management and development of urban areas
Note 1 to entry: This information encompasses a wide range of data, including demographic statistics, infrastructure
planning, environmental impact assessments, transportation systems, public services, and other factors that influence
the functioning and development of urban environments.
Note 2 to entry: Urban management information plays an important role in guiding policies and strategies for
sustainable urban development, infrastructure improvement and the overall well-being of urban populations.
3.1.3
indicator
quantitative, qualitative or descriptive measure
Note 1 to entry: The indicators in this document can refer to ISO 37120, ISO 37122, ISO 37123 or other sources that are
suitable for urban areas.
[SOURCE: ISO 6707-3:2022, 3.10.8, modified — Note 1 to entry has been added.]
3.1.4
dataset
collection of data with a shared format
Note 1 to entry: In this document, the term "dataset" refers to a collection of data that serves as the foundation for
creating indicators. By evaluating the dataset, it is possible to create various indicators.
[SOURCE: ISO/IEC 22989:2022, 3.2.5, modified — Note 1 to entry has been changed.]
3.1.5
scenario
description of the sequence of events from the user’s perspective to perform a task in a specified context
[SOURCE: ISO/TS 14812:2022, 3.1.12.2, modified — use case related terms has been deleted.]
3.2 Terms related to artificial intelligence (AI)
3.2.1
data mining
computational process that extracts patterns by analysing quantitative data from different perspectives
and dimensions, categorizing them, and summarizing potential relationships and impacts
[SOURCE: ISO/IEC 22989:2022, 3.1.11]
3.2.2
inference
reasoning by which conclusions are derived from known premises
Note 1 to entry: In AI, a premise is either a fact, a rule, a model, a feature or raw data.
Note 2 to entry: The term "inference" refers both to the process and its result.
[SOURCE: ISO/IEC 22989:2022, 3.1.17]

3.2.3
prediction
primary output of an AI system when provided with input data or information
Note 1 to entry: Predictions can be followed by additional outputs, such as recommendations, decisions and actions.
Note 2 to entry: Prediction does not necessarily refer to predicting something in the future.
Note 3 to entry: Predictions can refer to various kinds of data analysis or production applied to new data or historical
data (including translating text, creating synthetic images or diagnosing a previous power failure).
Note 4 to entry: Data generated from predictions have to be documented for users.
[SOURCE: ISO/IEC 22989:2022, 3.1.27, modified — Note 4 to entry has been added.]
3.2.4
artificial intelligence system
AI system
engineered system that generates outputs such as content, forecasts, recommendations or decisions for a
given set of human-defined objectives
Note 1 to entry: The engineered system can use various techniques and approaches related to artificial intelligence
(AI) to develop a model to represent data, knowledge, processes, etc. which can be used to conduct tasks.
Note 2 to entry: AI systems are designed to operate with varying levels of automation.
Note 3 to entry: It is important to be aware of the risks associated with the use of machine learning (ML) in AI
systems. ML algorithms heavily rely on the quality and accuracy of the data used for training. Additionally, ML models
sometimes act as black box methods like neural networks and have the potential for tampering with human-designed
methods. It is crucial to carefully evaluate the risk of using AI systems.
[SOURCE: ISO/IEC 22989:2022, 3.1.4, modified — Note 3 to entry has been added.]
3.3 Terms related to data
3.3.1
metadata record
record containing a description of a resource
[SOURCE: ISO 24622-1:2015, 2.10]
3.3.2
test data
evaluation data
data used to assess the performance of a final model
Note 1 to entry: Test data is disjoint from training data and validation data.
[SOURCE: ISO/IEC 22989:2022, 3.1.14]
3.3.3
data quality checking
process in which data is examined for completeness, bias and other factors which affect its usefulness for an
AI system
Note 1 to entry: It is important to ensure data quality regardless of whether an AI system is being used or a simple
exploratory analysis is performed.
[SOURCE: ISO/IEC 22989:2022, 3.2.2]

3.3.4
data sharing
the making of data accessible to others, where the data is structured by a schema
[SOURCE: ISO 10303-2:2024, 3.1.278]
4 Understanding the context of the appraisal framework
4.1 General
This clause describes the context of the appraisal framework for datasets and data processing methods that
create urban management information. It covers class models, indicator classes, processing classes, and
dataset classes, which are detailed in the following clauses, to help users better understand the framework
effectively and efficiently.
There are a series of preconditions to use this document, as set out below:
— before using this document, a set of indicators, including those derived from ISO 37120, ISO 37122,
ISO 37123 or other indicators such as the United Nations Industrial Development Organization (UNIDO)
Eco-Industrial Parks Indicators, should be selected for specific use;
— a management system for sustainable development has been established by the organization that wishes
to establish this appraisal framework. ISO 37101 and ISO 37104 provide requirements and implementation
guidance for the management system for sustainable development for cities and communities;
— the city has the capability to collect data, through smart city systems or other management methods;
— there is sufficient data that can be obtained from the management system or smart platform;
— members of management organizations in a city or community should undergo training and have a
comprehensive understanding of various data types and structures. This includes knowledge of data
definition, data sources, indicator calculations and strategic utilization of data relevant to their work;
— organizations should ensure the privacy and security of data. For example, all interested parties involved
are prohibited from directly disclosing the raw data and basic data in smart city systems. Instead, the
data holder or data processors (see 4.5) can process indicators based on the user’s needs and then
provide these indicators for use.
4.2 Objectives for creating urban management information
Once an organization decides to utilize this document for creating urban management information to aid
decision-making, it is recommended to define objectives with the considerations as below:
— achieving sustainable development as described in ISO 37101 and ISO 37104;
— meeting the requirement of smart city operation model as illustrated in ISO 37106;
— achieving the six sustainability purposes and addressing the twelve issues of ISO 37101;
— taking a lead in technology development and trends, such as AI, big data, Internet of Things (IoT), etc;
— establishing a set of indicators and implementing action plans;
— complying with the principles of data privacy and security;
— supporting data sharing for sustainable development.
NOTE Principles of data privacy and security refer to ISO/IEC 29100.

4.3 Principles for establishment of appraisal framework
4.3.1 General
A city or community should follow the principles outlined below to establish an appraisal framework that
covers indicators, processes, data, and datasets. These principles represent sub-principles of the four
delivery principles for a smart city, as provided in ISO 37106: visionary, citizen-centric, digital, open, and
collaborative.
4.3.2 Principles related to the visionary community
The ISO 37106 principle of a visionary community requires community leaders to build a clear, compelling
and inclusive city vision. This document supports this through the following sub-principles:
— compatibility with future scenarios: AI systems and geographic information technology should be
adopted to aid the analysis and creation of big volume data generated out of cities and communities;
— continuous feedback: application of the appraisal framework supports visibility of the data assets and
processing algorithms that are used to support indicator generation. This supports critical appraisal of
data gaps, poor data processing and inappropriate indicators, helping to ensure sustainability goals are
achieved.
4.3.3 Principles related to the citizen-centric community
The ISO 37106 principle of a citizen-centric community emphasizes the importance of a detailed and
segmented understanding of the needs of citizens and businesses in a city or community. This document
supports this through the following sub-principles:
— user focused: it can be used by data originators, data providers and users to facilitate sharing of trusted
data and to enable better decision-making and investment. This can also ensure equity and inclusiveness
for these participants;
— result oriented: it can evaluate the data availability while considering transparency and accountability,
e.g. whether sufficient data has been generated to support sustainable development purposes of human
settlement like cities, regions and buildings;
— privacy and security protected: when implementing this appraisal framework, particularly in relation to
the utilization of open data and ensuring compatibility with AI systems, it is crucial to take into account
potential risks and prioritize the protection of privacy and security. This includes preventing any
malicious purposes, such as privacy breaches, data tampering, data loss and threats to urban security.
4.3.4 Principles related to the digital community
The ISO 37106 principle of a digital community requires enabling the ubiquitous and integrated digitisation
of a city or community. This document supports this through the following sub-principles:
— technology neutral: this document does not specify any particular technology implementation. Indeed,
this document recognizes that many approaches to collecting data exist and this can increase with
more ubiquitous IoT sensors. This document does, however, aim to make explicit the advantages and
disadvantages of different approaches to supporting sustainable development in a particular context;
— implementation focused: this document enables the collection and aggregation of trusted data and gives
recommendations on how to generate value from data;
— semantic interoperability: this document ensures that different systems, platforms, or organizations
can understand and interpret each other's data, semantics, and meanings during data exchange and
information sharing. The emphasis is placed on achieving compatibility between different department
systems and platforms to promote cross-organizational, cross-platform, or cross-domain information
exchange and sharing.
4.3.5 Principles related to the open and collaborative community
The ISO 37106 principle of an open and collaborative community encourages creating spaces and
opportunities for new collaborations. This document supports this through the following sub-principles:
— simple to use: it is written in clear and straightforward language or utilizes a semantically interoperable
programming language, making it eas
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