SBD strategy for UN Habitat Smart Safer City171015

DATEㅣ2017. 10. 15

Junyoung Choi, Ph.D. in urban planning & GIS

Spatial Big Data Team at MOLIT

Spatial Information Office, LH

Charter Member, OSGeo

Spatial Big Data Strategy for

UN Habitat Smart Safer City

Presention is about my personal opinion and is not related to UN Habitat’s official direction

October 5, 2015, Open Source GIS for Rapid Urban Growth and Land Management, UN-GGIM-AP,

UN-ESCAP, UN-HABITAT/GLTN Joint Workshop on Land Administration and Management, Jeju,

South Korea.

September 16, 2015, FOSS4G for Rapidly Urbanizing Cities and UN Sustainable Development

Goals(SDGs); SDG 11 Cities and Human Settlement, LH-OSGeo Joint Conference on the Open Source for

UN and Developing Countries, FOSS4G Seoul 2015, Seoul, South Korea.

August 24, 2106, Achieving Urban SDG and New Urban Agenda using the Open Geospatial Data of the

International Organizations, FOSS4G Bonn 2016, Bonn, Germany.

March, 2016, Supporting the measurement of the United Nations’ sustainable development goal 11

through the use of national urban information systems and open geospatial technologies: a case study of

south Korea, Open Geospatial Data, Software and Standards 1(4).

October, 2016, What drives developing countries to select free open source software for national

spatial data infrastructure?, Spatial Information Research 24(5), pp 545–553.

My presentations on Global Urban Goals & FOSS4G**FOSS4G: Free Open Source Software for Geospatial

FOSS4G-ASIA 2017

January, 2017, FOSS4G for Global Urban Goals and K-Smart City, FOSS4G ASIA 2017, Hyderabad,

India.

September, 2017, Spatial Big Data Strategy for CityLab on Smart Safer City, UN HABITAT Urban Thinkers Campus Seoul: How Smart Technology Makes Cities Safer?, Seoul, Korea

November 15, 2017, Outstanding : One panel discussion and one presentation, “Security, Democracy

and Cities: Coproducing Urban Security Policies” organized by European Forum for Urban Safety(EFUS),

Barcelona, Spain.

1 l UN HABITAT Safer City

2 l Spatial Big Data Strategy for Safer City

3 l Case study : Safe Route for Chungju City

Contents

4 l How Smart Technology Makes Cities Safer?

#1UN Habitat Safer City

Manifestations of Urban Crime

• The fortification of cities through the “architecture

of fear” and the rapid expansion of surveillance a

nd walled cities are all testament to the shared h

eritage of urbanization and security.

• Crime is impacting more on the urban poor (who liv

e largely in unplanned settlements) – contrary to c

ommon perceptions.

Photo Credit: UN Habitat(2017)


UN support – The Safer Cities Programme

In 1996 UN-HABITAT established a ‘urban crime prevention programme’ to assist cities to develop crime prevention initiatives and thereby reduce incidence and impact of crime and violence in cities.

The Programme operates through:

– Direct support to cities that intend to formulate and implement crime prevention strategies

– Support to networking and city-to-city collaboration

– Development and dissemination of tools

– Advocacy and policy development on crime prevention issues - gender, youth-at-risk, role of local government


Three pillars of prevention

SOCIAL PREVENTION

Youth and Women

Youth empowerment

Victim support

Recreational facilities to occupy youth

Developing victim support

LAW ENFORCEMENT

AND CJS REFORM

Targeted visible police patrols

Conflict resolution

Neighborhood watch

By-law enforcement

Improve relationships and

accessibility

URBAN DESIGN

Supporting street layout

Improving street lighting

Designing streets, buildings, parks etc. to reduce opportunities for

crime

Reorganize markets or terminals


Towards Human Settlement Vulnerability Reduction

Security of

Tenure

Targeting land and housing

evictions and associated

violent conflicts

Natural

Disasters

Targeting risk reduction,

preparedness

and resilience

Crime, Violence & Social

Cohesion

* Targeting urban vulnerability

reduction

to crime and violence

* Building on social capital of

communities

* Focusing on social

interventions


Safer Cities Process

Key Elements for Effective Implementation

A COALITION

• with leadership

• assembling all key partners

• sensitive to age, gender &

cultural differences

• supported by a secretariat

• engaging citizens

• a communication strategy

A security diagnosis

• challenges

• risk factors

• community resources

A strategy and action plan

• establish priorities

• identify model for practices

• target actions on risk factors

• balance short & long

term actions

Implementation

• training

• co-ordination of partners

• actions

Evaluation

& Feedback

• process evaluation

• impact evaluation

• tools development

Regional and (inter)

national networks fo

r exchange and repli

cation


Safer City 2.0


Overview of CityLab as Pilot Action Site

Knowledge

City Lab will connect cities to new and inspiring sources

of knowledge that can be adapted to the local contexts

to inform more effective policy responses as well as pra

ctice.

LearningProviding learning opportunities for the urban practitio

ner - using existing context specific practices; action-lea

rning seminars; city to city learning through structured

exchange visits and other means.

Supporting

InnovationTesting innovative approaches in cities in a range of are

as and validate their applicability.

Facilitating

Solutions

Provider of high quality technical expertise and facilitato

r of change within cities . Drawing on partner networks

and network cities– arranging and sequencing support a

nd processes to provide a sustainable solution.

Safer Cities in

Sutainable Dev. Goal(SDG)

Source: https://unhabitat.org/wp-content/uploads/2016/02/SDG-Goal%2011%20Monitoring%20Framework%2025-02-16.pdf

11.7.1 The average share of the built-up area of

cities that is open space in public use for all

disaggregated by age group, sex and persons with

disabilities

11.7.2 Proportion of women subjected to physical or

sexual harassment by perpetrator and place of

occurrence (last 12 months)

• Need to disaggregated indicators

• Defining urban and city

• Working with spatial indicators and data

Safer Cities in

New Urban Agenda(NUA)

100. Supporting the provision of

well-designed networks of safe,

accessible, green and quality streets

and other public spaces &

103. Integrating inclusive measures

for urban safety and the prevention

of crime and violence

Source: https://unhabitat.org/wp-content/uploads/2016/02/SDG-Goal%2011%20Monitoring%20Framework%2025-02-16.pdf

Supporting the monitoring of safe city in the

measuring the indicators of SDG and NUA

by international org. and central gov.

#2Spatial Big Data Strategy for Safer City

What is Spatial Big Data

ㆍRoad

ㆍParcel

ㆍZoning

ㆍDEM

ㆍ3 dimensional

ㆍFloating pop.

ㆍCredit card

ㆍDocuments

ㆍLand transaction

ㆍTraffic

•Road/Building/Stream/Parcel/Zoning…

•Flooded area, Susceptible coastal flood, Land slide…

•DEM, Aerial photo, R.S., 3 dimensional data…

Spatial data(Vector/Raster)

•SNS, Blog, News…

•Floating pop./Credit card…

•Photos/Movies…

Big Data

(Private)

•Document issuance of land regulation etc.

•Land transaction/Rental housing contract…

•Traffic/Transportation…

•Geosensor, CCTV…

Big Data

(Public)

Clickstream/Query word

JoiningGeocodin

g

•••

•••

Geo-parsing

Parcel

Administrative bnd.

POI

Over 80% of Big Data is also geographically referenced!

Source: Kim, D.J.(2014)”Geospatial Big Data for Gov. 3.0”

What is Spatial Big Data

Prompt/accurate/precise diagnosis Customized/effective prescription

Simul-ation

Spatial analytics

Visuali-zation

Diagnosis Alternatives Implementation Evaluation

ㆍRoad

ㆍParcel

ㆍZoning

ㆍDEM

ㆍ3 dimensional

ㆍFloating pop.

ㆍCredit card

ㆍTraffic

ㆍDocuments

ㆍLand transaction

•••

•••

EnvironmentPhysical & logical

Behavior

Planning

Opinion


Spatial Big Data Platform

…

[Population] [Permits] [Registries]

Big Data(MOSPA)

Private

[Credit card, Floating pop…][Road]… [Building] [3D image]

…

NSDI Spatial Big Data(MOLIT)Public

ㆍLand trans. / Document issuance

ㆍLand reg. / Rental contract

ㆍTraffic accident / Traffic volume

ㆍTravelling etc.

BD in public

ㆍFloating pop.

ㆍLand transaction

ㆍRental contract

ㆍDocuments

ㆍTraffic

ㆍCredit card

Hadoop Spatial Hadoop

ㆍSNS, Blog, Cafe...ㆍVGI

BD in private

Parcel map

Administrative

POI

JoiningGeocoding

Geoparsing

& Geocoding

Fusion


spatialbigdata.mp4

Smart + Safer City

A COALITION

• Social Media

• Cloud Computing

• FOSS

(Free Open Source Software)

A security diagnosis

-Big Data

-Social Media

-Crowdsourcing

-Open Data

A strategy and action plan

- Big data

- GIS data

- Lidar, BIM(3D data)

Implementation

• Sensors

• CCTV

• IoT

• Drone, 3D printing

Evaluation

& Feedback

• Social Media

• Big Data

• Drone

Regional and (inter)

national networks

for exchange and

replication

1st step: Adopting smart technology to the Safer City

Photo Credit: JY. Choi(2016) Smart City Lounge, Jakarta, Indonesia

Smart + Safer City

2nd step: Identifying and collaborating with Stakeholders

Research

InstitutionLocal &

central gov.

Police

OfficeNPO

IT

company

A COALITION

A security

diagnosisA strategy and

action plan

ImplementationEvaluation

& Feedback

Regional &

international

Networks for

exchange &

replication

Citylab on Smart Safer City

Knowledge

When applying a smart safer city knowledge, it is needed

to consider the technological infrastructure and maturity

of society’s adoption of smart technology

LearningFocus more on bridging digital divide and raise smart citi

zen who will be familiar with smart technology and lead

the smart technology based practices

Supporting

Innovation

Testing smart technologies inside Smart Safer City throug

h the living lab approach by participating citizens to appli

cation processes of Smart Safer City solutions

Facilitating

Solutions

Collaboration with open source and standardization orga

nizations to lower adoption and maintenance cost and al

leviate the technological barriers

Applying spatial big data to

the three pillars of safer city

Source: Safer Cities City Changer Toolkit(www.worldurbancampaign.org)

Time-series analysis to

evaluate the practice

Identifying groups at risk

when analyzing

Spatial analysis using big data

#3Case Study: Safe Route for Chungju City

Crime status in Chungju

from 2014 to 2016Security

DiagnosisA Strategy &Action Plan

ImplementationEvaluation &

Feedback

# of crime

SecurityDiagnosis

A Strategy &Action Plan


Feedback


from 2014 to 2016

# of crimeIn night

time

# of crimein daytime

SecurityDiagnosis



Feedback

1631

368 35227 44 3

요약

범죄발생 현황

폭력

청소년비행

절도

성폭력

변사자

강도

Violence

Violence

Juvenile delinquency

Sexual assault

Murder

Robbery

Robbery

Juvenile

delinquency

Theft

Sexual assault

Murder

Crime occurence


from 2014 to 2016

Theft


SecurityDiagnosis


Feedback

Night time crime occurrence

Facilities vulnerable to crime at night

Facilities related to enhance safety

Population by mobile phone

Crime risk index along the pedestrian network

Ranking(Vulnerable)

Administrative boundary

District Ration

1st 성내·충인동 15 구역 22.4%

12th 호암·직동 0 구역 0%

Crime Risk Index Framework


SecurityDiagnosis


Feedback


• Gaussian Kernel Density using population of night time

• Gaussian Kernel Density using crime occurrence of night time


SecurityDiagnosis


Feedback


Indoor parking

Vacant house

Entertainment venue

One-room


SecurityDiagnosis


Feedback


Bus stopCCTVStreetlightStreetlight with CCTV

ImplementationA Strategy &Action Plan

SecurityDiagnosis

Evaluation &Feedback

Safe Route Analysis

Comparison of index and crime



ImplementationSecurity

Diagnosis

Comparison of index and CCTV




Diagnosis

Comparison using Community mapping




Diagnosis

Application of the index




Diagnosis

#4How Smart Technology Make Cites Safer

Future directions

Relationship

with SDG

Achieving SDG through Smart Safer City program need to

have a close relationship with SDG goals. Therefore, Sma

rt Safer Citylab activities should support the achieving th

e goals.

Analysis

framework

To perform a spatial big data analysis, there have to be a

analysis framework which contains standardized indicato

rs and models

Identify vulne

rable class

Vulnerable classes such as woman, youth, elder have to

be considered when finding a result using mobile phone

data

Acquisition of

data

The most important thing is an acquisition of data. Befor

e applying this method, related data have to be collected

beforehand.

Future directions

Smart City Evaluation System (FOSS4G based)

Smart Safer City Award

EvaluationFramework

City Prosperity Index(CPI)

Smart CityReadiness Index

Database

Urban Open Data Portal(urbandata.unhabitat.org)

Proposed cities’ database

Future directions

• World Urban Forum 9- 7~13 February, 2018, Kuala Lumpur- http://wuf9.org/

• European Forum for Urban Safety- 15~17, November, 2017, Barcelona- http://efusconference2017.eu/

Thank you

Spatial Big Data CenterMinistry of Land, Infrastructure and Transportation(MOLIT)

Junyoung Choi (Ph.D in Urban Planning & GIS)[email protected], [email protected]

Spatial Information OfficeKorea Land and Housing corp.

mailto:[email protected]

mailto:[email protected]

SBD strategy for UN Habitat Smart Safer City171015

Technology

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