DIPARTIMENTO DISCIENZE AMBIENTALI, INFORMATICA e ...acadia/wp-content/... · • SpagoBI to represent and interact with the data on the maps – Government people appreciates transposition

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Dipartimento di Scienze Ambientali, Informatica e

Statistica, Università "Ca' Foscari" di Venezia

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Direzione Sistemi Informativi

work partiallysupportedby

A. Candiello & A. Cortesi

KPI-supported PDCA …38

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KPI-Supported PDCA Model for Innovation

Policy Management in Local Government

IFIP E-Government Conference 2011

Antonio Candiello – Agostino Cortesi

DAIS – Dipartimento di Scienze

Ambientali, Informatica e Statistica,

Università “Ca’ Foscari” di Venezia

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Summary

1. A PDCA Model for eGovernment

• the approach

• the eGovernment Intelligence framework

• the software modules

2. Technology & Maps

• webbots, data scrapers, spiders

• the open source SpagoBI engine

• accumulating data in DBs

3. Ongoing Research: ICT-related gender gap KPIs

• search for scientific collaborations

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Main Goals

• finding an objective validation for the effectiveness of eGovernment and ICT innovation projects,

• qualifying and quantifying the effectivenessthrough appropriate impact statistical territorial indicators,

• gathering the relevant indicators via automaticwebbots/scrapers and semiautomaticextractors/wrappers, completing the data when needed with focused survey campaigns,

• representing and mapping the indicators showing the explicit relation with the affectinginnovation projects and the areas involved.

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The Approach

1. Adapt the PDCA improvement cycle for Local Government (LG) Policies

2. Use validated official data from Public Authorities and European/National/Regional Institutions – fully reliable

3. Add “raw” data, less reliable but more frequently updated

4. Geo-refentiate the data at municipalities level

5. Store the KPI obtained every day (sort of “KPI Wayback Machine”)

6. Apply this to ICT innovation projects but also to…

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Do: run projects,

begin measurements,

monitor KPIs.

Check: analyse indicators,

verify goal achievements,

view georeferentiate data.

Act: review

policies,

redefine goals.

Plan: define

local policies,

select innovation KPIs,

set goals.

DO

CHECK

AC

T

Policy

ManagerPLAN

A PDCA Model for eGovernment

Policy

Reviewer

Geodata

Viewer

Impact

Monitor

Event

Scheduler

Data

Mining

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Plan

Do

DefinitionLocal Policies for

ICT Innovation

InterventionICT Innovation

Projects Started

VerificationImpact Analysis of ICT Projects

AssessmentReview of Policiesfor ICT Innovation

Act

Check

TargetingChoice of

Indicators and (Staggered) Goals

MonitoringMeasurement of

ICT Innovation Indicators

AnalysisGeo-visualization

and StatisticalAnalysis of Data

EvaluationIdentification of

Critical Points

Po

lic

y

Ma

na

ge

me

nt

eG

ov

ern

men

t

Inte

llig

en

ce

eGovernment Intelligence

for Policy ManagementT

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ICT Innovation Policies

Projects

Milestones

Impacted

Territorial KPIs

KPI Checks

Targets

Plan

Do Act

Check

Plan

Time

DefinitionLocal Policies for

ICT Innovation

TargetingChoice of

Indicators and (Staggered) Goals

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itPlan

Do Act

Check

Do

indirect feed

KPIDatabase

spiders &

webbots

spiders &

webbotsraw webraw web

keywordsHF channel

high frequency

direct feed

online DBs

LF channellow frequency

data

adapters

sourcessources

Indicatore #1Indicatore #1

Indicatore #1Indicatore #1

survey #1survey #1

citizens feedback on

ICT use & services surveys on ICT

survey

campaigns

staggered data consolidation

InterventionICT Innovation

Projects Started

MonitoringMeasurement of

ICT Innovation Indicators

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business intelligence

layer modelgeo-visualization KPI dbms

Web Extractors

Low Frequency (LF), High Reliability Channel

Semi-automatic

Webbots & Scrapers

High Frequency (HF), Low Reliability Channel

Automatic

Surveys

Highly Focused but Costly,Medium Reliability Channel

Manual / partly web assisted

Plan

Do Act

Check

CheckVerificationImpact Analysis of ICT Projects

AnalysisGeo-visualization

and StatisticalAnalysis of Data

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itPlan

Do Act

Check

Act

ICT Innovation Policies & Projects Review

chan

ge

Ok Ko

AssessmentReview of Policiesfor ICT Innovation

EvaluationIdentification of

Critical Points

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Software Modules

• Policy Manager [PLAN], to input/define policies, projects, actions,

• Event Scheduler [DO], controlling the wake-up of data collectors’ daemons,

• Impact Monitor [DO], managing webbots, adapters & scrapers,

• Geodata Viewer [CHECK], to visualize the data via maps & tables via SpagoBI,

• Policy Reviewer [ACT], to modify the policies & projects,

• Data Miner [WHAT-IF],(ongoing research) a “ Project/Policy Simulator”

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it KPIs from data sources

and their representation

• Currently:– “Official” Data collected via adapters/extractors or online

webbots. High quality data.

– “Raw” Data collected via webbots/scrapers/spiders on the web (the easiest: # of results of searches). Low quality data.

• In the next future: – Open Government Data (eGovernment),

– Open Linked Data (semantic web)

– also: Local Government could also collect data from different (& raw) web data sources, validate data and expose. Needed data quality assurance activity

• Data is accumulated daily/weekly/monthly in a DB (twitter scraping would need higher frequencies)

• Geo-referentiation of data: at regional, provincial, municipality level

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Advantages of technology used

• Java (also used PHP and Python) for data daemons (webbots/scrapers/adapters)

– specific webbots/scrapers for each social or business network/community; needed elaborations of data retrieved. Data updated daily. Host sites could discourage access to web robotsdifficult to maintain

– specific adapters for each Institutional online or offline (e.g. csvfiles) data source. Eurostat offers a wide set of formats & access modalities. However: data updated yearly or monthlyeasy to maintain

• SpagoBI to represent and interact with the data on the maps

– Government people appreciates transposition of data on maps

– Patterns of effectiveness of policies are clearly visible

• Postgres DB to deposit the data

– Accumulating data makes possible to analyse the growth trends

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Accessing the big Web Repositories

• Youtube– used the Youtube APIs for extracting KPIs on video production & consume

(#uploads, #views)

– also: web parsing youtube site

• Facebook:– not using Facebook APIs (as users have to agree to use the application): we

extract data with general web parsing techniques)

• Google: – not using Google Search APIs (limited to max 100 daily searches)

– using Google Analytics APIs for (owned/managed) web site accesses

– Limits on number of searches that could be done in a period of time.

• Yahoo: – not using Yahoo! Search APIs (limited to max 100 daily searches)

– using Yahoo Sites APIs for web sites size and relevance (#pages, #inlinks)

– Limits on number of searches that could be done in a period of time.

• General Web:– HTML Cleaner APIs for (bad-formed HTML) web parsing (for Java)

– used Schrenk’s webbots library for PHP

– Blogs and forums are the more interesting sources of information (frequently updated and massive)

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it Youtube KPIs: video uploads & views

(Belluno province, municipalities)

Belluno 506 35035

Feltre 75 36556

Santa Giustina 56 30088

Borca di Cadore 53 6533

Sedico 52 10061

Agordo 37 17231

Cesiomaggiore 26 29046

Domegge di Cadore 21 6976

Pedavena 17 26246

Mel 17 26748

Longarone 12 74633

Sappada 11 11225

Limana 11 6953

Trichiana 10 8695

Selva di Cadore 10 8960

Lozzo di Cadore 10 2015

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it Mapping the data on the territory:

content production & consume

Youtube – uploads(producers, inhomogeneous)

Youtube – views(consumers, more homogeneous)

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it Example KPIs (Veneto Region,

municipalities > 25.000 inhabitants)

Dati

comune

Popula

tion

Are

a (k

mq)

Tota

l Incom

e

Incom

es

# o

f Schools

n.v

ideo.y

outu

be

n.v

iew

s.y

outu

be

n.h

its.y

ahoo

n.p

ages.e

gov

n.in

links.e

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n.IC

Tente

rpris

es

%.w

ide b

and

Venezia 270.801 413 € 4.089.444.597 163.323 231 1.417 450.274 7.750.000 232.772 60.572 146 100,0%

Verona 264.475 207 € 4.030.822.293 153.339 276 1.453 396.203 2.520.000 14.033 20.525 237 100,0%

Padova 212.989 93 € 3.596.976.438 123.442 216 1.355 177.417 3.500.000 2.880 6.620 311 100,0%

Vicenza 115.550 81 € 1.719.538.821 65.709 128 1.269 479.754 2.620.000 36.230 43.927 132 100,0%

Treviso 82.208 56 € 1.405.295.446 48.569 103 1.248 131.630 2.570.000 8.397 9.883 110 100,0%

Rovigo 52.118 109 € 747.269.908 31.361 64 1.052 35.704 1.770.000 8.180 5.872 60 95,0%Chioggia 50.772 185 € 513.947.658 25.127 44 24 6.707 78.900 4.804 2.081 28 92,0%

Bassano del Grappa 43.015 47 € 590.078.083 23.737 50 151 20.301 154.000 5.630 4.216 52 100,0%

San Dona di Piave 41.247 79 € 541.208.071 23.339 31 87 4.772 103.000 6.659 1.703 23 95,0%

Schio 39.586 67 € 538.858.210 23.305 35 197 17.680 881.000 9.597 7.216 34 100,0%

Mira 38.857 99 € 499.994.000 22.727 28 10 1.375 2.310.000 162 3.071 36 90,0%

Belluno 36.618 147 € 577.581.894 23.157 49 1.104 66.209 1.310.000 2 5.374 38 100,0%

Conegliano 35.676 36 € 530.114.920 20.611 55 245 60.795 181.000 11.958 6.484 35 100,0%

Castelfranco Veneto 33.675 51 € 454.959.141 18.907 53 93 41.112 146.000 1 146 36 100,0%

Villafranca di Verona 32.866 24 € 419.241.447 18.657 11 545 60.065 92.200 534 70 43 100,0%

Montebelluna 30.948 49 € 406.756.610 17.342 34 140 10.236 89.000 4.753 4.090 41 100,0%

Vittorio Veneto 29.210 83 € 395.514.950 17.159 45 207 9.651 611.000 4.173 1.542 45 91,0%

Mogliano Veneto 28.125 46 € 430.201.499 16.393 28 149 41.962 27.300 3.679 2.265 31 95,0%

Valdagno 26.829 50 € 362.690.849 17.203 28 126 68.010 19.300 12.107 2.897 18 95,0%

Mirano 26.795 46 € 383.648.244 15.722 30 296 12.547 203.000 3.204 699 137 100,0%

Spinea 26.674 15 € 371.654.778 16.066 20 5 1.337 40.600 5.500 976 40 100,0%

Arzignano 25.823 34 € 328.905.490 13.837 22 155 14.227 87.300 2.138 754 62 100,0%

Legnago 25.556 80 € 339.314.492 14.996 33 223 12.599 76.300 3.208 750 34 100,0%

Portogruaro 25.406 102 € 351.142.820 15.055 30 166 23.712 181.000 3.657 16.889 26 90,0%

Jesolo 25.232 95 € 301.490.304 14.884 20 681 63.013 140.000 2.919 403 20 100,0%

Signs of good community vitality Bad data (name match)

peak of uploaded videos

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it Mapping the data on the territory:

Work & Education

# of ICT Enterprises(work)

# of Schools(education)

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it Different KPI layers

(Belluno province)

Income

Population Search Engine,

ICT-related

Youtube uploads

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Institutional Data: Eurostat

An h

igh

lyre

liab

lesourc

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fdata

.

Yearl

yup

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s.

Som

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ata

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(see

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Eurostat “NUTS” & LAUs

Level Code NUTS-Code Description

3 2920 ITC1 Piemonte

3 2930 ITC2 Valle d'Aosta/Vallée d'Aoste

3 2940 ITC3 Liguria

3 2950 ITC4 Lombardia

3 2960 ITD1 Provincia Autonoma Bolzano/Bozen

3 2970 ITD2 Provincia Autonoma Trento

3 2980 ITD3 Veneto

3 2990 ITD4 Friuli-Venezia Giulia

3 3000 ITD5 Emilia-Romagna

3 3010 ITE1 Toscana

3 3020 ITE2 Umbria

3 3030 ITE3 Marche

3 3040 ITE4 Lazio

3 3050 ITF1 Abruzzo

3 3060 ITF2 Molise

3 3070 ITF3 Campania

3 3080 ITF4 Puglia

3 3090 ITF5 Basilicata

3 3100 ITF6 Calabria

3 3110 ITG1 Sicilia

3 3120 ITG2 Sardegna

3 3130 ITZZ Extra-Regio

The upper LAU level (LAU level 1, formerly

NUTS level 4) is defined for most, but not all

of the countries.

The lower LAU level (LAU level 2, formerly

NUTS level 5) consists of municipalities or

equivalent units in the 27 EU Member

States.(>120,000 LAU2 !!!)

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Competition between regions !T

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Ongoing Research: Gender Gap

• The decreasing rate of the number of women applyingfor Computer Science courses is even higher than the overall one. The number of enrolled women abandoning the courses is higher than the number of abandoning men as well.

• Women have difffffffferent motivations and interests for studying than men. Indeed, rather than enjoying computers for some kind of ‘hacking pleasure’, more women than men are interested in other disciplines as well.

• Their special interest in Computer Science is often connected either to its application to other fields like Medicine, Social Sciences, Astronomy and so on, or e.g. to its strong connections to Logic, which often attracts them despite the prejudices about the so-called non-logical aptitude of women.

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Gender-sensitive contents on the raw web

• We are searching for collaborations please contact us if you are interested in this field of research

• We need a strategy to extract gender-sensitive “raw web” contents data

• We are experimenting counting pages with some gender-related words in italian language (more gender-dependent than english)– “sono nato/nata a” (born in, common in blogs and CVs), work-

related

– “sono interessato/interessata a” (interested in, common in announces), culture-related

– “sono stato/stata a” (was in) travels-related

• Other possible strategies: – search for the people first names and measure ratio of

female/male hits

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itRegione Maschile Femminile Totale GenderRatio Res% Ratio PopolazionePop%

Abruzzo 133.000 22.300 155.300 16,77% 2,18% 1,0 1.342.366 2,21%

Basilicata 76.700 16.100 92.800 20,99% 1,30% 1,3 587.517 0,97%

Calabria 1.120.000 49.800 1.169.800 4,45% 16,45% 5,0 2.011.395 3,32%

Campania 166.000 28.800 194.800 17,35% 2,74% 0,3 5.834.056 9,62%

Emilia Romagna 142.000 25.800 167.800 18,17% 2,36% 0,3 4.432.418 7,31%

Friuli-Venezia Giulia 78.000 13.400 91.400 17,18% 1,29% 0,6 1.235.808 2,04%

Lazio 223.000 55.600 278.600 24,93% 3,92% 0,4 5.728.688 9,45%

Liguria 542.000 24.900 566.900 4,59% 7,97% 3,0 1.616.788 2,67%

Lombardia 390.000 42.900 432.900 11,00% 6,09% 0,4 9.917.714 16,36%

Marche 300.000 42.500 342.500 14,17% 4,82% 1,9 1.565.335 2,58%

Molise 119.000 21.800 140.800 18,32% 1,98% 3,8 319.780 0,53%

Piemonte 219.000 30.000 249.000 13,70% 3,50% 0,5 4.457.335 7,35%

Puglia 560.000 27.900 587.900 4,98% 8,27% 1,2 4.091.259 6,75%

Sardegna 596.000 28.800 624.800 4,83% 8,79% 3,2 1.675.411 2,76%

Sicilia 523.000 36.300 559.300 6,94% 7,86% 0,9 5.051.075 8,33%

Toscana 516.000 34.900 550.900 6,76% 7,75% 1,3 3.749.813 6,19%

Trentino 378.000 18.600 396.600 4,92% 5,58% 3,3 1.037.114 1,71%

Umbria 110.000 17.100 127.100 15,55% 1,79% 1,2 906.486 1,50%

Valle d'Aosta 72.000 12.600 84.600 17,50% 1,19% 5,6 128.230 0,21%

Veneto 256.000 42.300 298.300 16,52% 4,19% 0,5 4.937.854 8,14%

ITALIA 6.519.700 592.400 7.112.100 9,09% 60.626.442

Ricerca su Google di "sono interessata/o"+[REGIONE], differenza di genere e rapporto su valori demografici

Gender-related contents “interested”O

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Gender-related contents “interested”

Provincia Maschile Femminile Totale GenderRatioRes% Ratio PopolazionePop% #Comuni

Belluno 633.000 28.500 661.500 4,50% 10,25% 2,4 213.474 4,32% 69

Padova 764.000 82.900 846.900 10,85% 13,13% 0,7 934.216 18,92% 104

Rovigo 644.000 33.300 677.300 5,17% 10,50% 2,1 247.884 5,02% 50

Treviso 1.040.000 55.200 1.095.200 5,31% 16,97% 0,9 888.249 17,99% 95

Venezia 1.060.000 79.900 1.139.900 7,54% 17,67% 1,0 863.133 17,48% 44

Verona 1.130.000 111.000 1.241.000 9,82% 19,23% 1,0 920.158 18,63% 98

Vicenza 727.000 63.300 790.300 8,71% 12,25% 0,7 870.740 17,63% 121

VENETO 5.998.000 454.100 6.452.100 7,57% 4.937.854 581

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Gender-related contents “born in”

Regione Maschile Femminile Totale GenderRatio Res% Ratio PopolazionePop% #Comuni #Province

Abruzzo 266.000 39.300 305.300 14,77% 4,29% 1,9 1.342.366 2,21% 305 4

Basilicata 81.200 21.500 102.700 26,48% 1,44% 1,5 587.517 0,97% 131 2

Calabria 341.000 114.000 455.000 33,43% 6,40% 1,9 2.011.395 3,32% 409 5

Campania 727.000 45.900 772.900 6,31% 10,87% 1,1 5.834.056 9,62% 551 5

Emilia Romagna 152.000 33.700 185.700 22,17% 2,61% 0,4 4.432.418 7,31% 348 9

Friuli-Venezia Giulia 80.800 21.500 102.300 26,61% 1,44% 0,7 1.235.808 2,04% 218 4

Lazio 345.000 56.700 401.700 16,43% 5,65% 0,6 5.728.688 9,45% 378 5

Liguria 222.000 41.100 263.100 18,51% 3,70% 1,4 1.616.788 2,67% 235 4

Lombardia 334.000 59.500 393.500 17,81% 5,53% 0,3 9.917.714 16,36% 1.546 12

Marche 168.000 50.700 218.700 30,18% 3,08% 1,2 1.565.335 2,58% 239 5

Molise 70.200 20.900 91.100 29,77% 1,28% 2,4 319.780 0,53% 136 2

Piemonte 223.000 50.800 273.800 22,78% 3,85% 0,5 4.457.335 7,35% 1.206 8

Puglia 247.000 57.600 304.600 23,32% 4,28% 0,6 4.091.259 6,75% 258 6

Sardegna 184.000 64.500 248.500 35,05% 3,49% 1,3 1.675.411 2,76% 377 8

Sicilia 430.000 99.100 529.100 23,05% 7,44% 0,9 5.051.075 8,33% 390 9

Toscana 733.000 85.200 818.200 11,62% 11,50% 1,9 3.749.813 6,19% 287 10

Trentino 169.000 30.000 199.000 17,75% 2,80% 1,6 1.037.114 1,71% 333 2

Umbria 109.000 29.300 138.300 26,88% 1,94% 1,3 906.486 1,50% 92 2

Valle d'Aosta 57.700 13.400 71.100 23,22% 1,00% 4,7 128.230 0,21% 74 1

Veneto 283.000 69.500 352.500 24,56% 4,96% 0,6 4.937.854 8,14% 581 7

ITALIA 5.222.900 1.004.200 6.227.100 19,23% 60.626.442

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Gender-related contents “born in”

Provincia Maschile Femminile Totale GenderRatioRes% Ratio PopolazionePop% #Comuni

Belluno 45.100 15.000 60.100 33,26% 0,93% 0,22 213.474 4,32% 69

Padova 466.000 57.400 523.400 12,32% 8,11% 0,43 934.216 18,92% 104

Rovigo 37.000 13.500 50.500 36,49% 0,78% 0,16 247.884 5,02% 50

Treviso 111.000 39.400 150.400 35,50% 2,33% 0,13 888.249 17,99% 95

Venezia 413.000 104.000 517.000 25,18% 8,01% 0,46 863.133 17,48% 44

Verona 224.000 66.500 290.500 29,69% 4,50% 0,24 920.158 18,63% 98

Vicenza 196.000 41.000 237.000 20,92% 3,67% 0,21 870.740 17,63% 121

VENETO 1.492.100 336.800 1.828.900 22,57% 4.937.854 581

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it Eurostat Education domain

Gender-sensitive KPIs

• Education indicators - non-finance (t_educ_indic)

• Share of women among tertiary students (tps00063)

• Science and technology graduates by gender (tsiir050)

• Educational attainment, outcomes and returns of education (t_edat)

• Persons of the age 20 to 24 having completed at least upper secondary education by gender (tsiir110)

• Early leavers from education and training by gender(tsisc060)

• Life-long learning by gender (tsiem080)

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it Eurostat Work domain


• Labour market (t_labour)

• Average gross annual earnings in industry and services, by gender (tps00175)

• Gender pay gap in unadjusted form in % (tsiem040)

• Employment - LFS adjusted series (t_lfsi_emp)

• Employment growth by gender (tsieb050)

• Employment rate by gender, age group 15-64 (tsiem010)

• Employment rate of older workers by gender (tsiem020)

• Statistics on research and development (t_rd)

• Share of women researchers, by sectors of performance (tsc00005)

• Share of women researchers (FTE): all sectors (tsc00006)

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it Eurostat NUTS-level


• Persons aged 25-64 with lower secondary educationattainment, by sex and NUTS 2 level (%) (from 2008) (edat_lfse_09)

• Persons aged 25-64 with upper secondary educationattainment, by sex and NUTS 2 level (%) (from 2008) (edat_lfse_10)

• Persons aged 25-64 with tertiary education attainment bysex and NUTS 2 level (%) (from 2008) (edat_lfse_11)

• Persons aged 30-34 with tertiary education attainment, bysex and NUTS 1 level (%) (from 2008) (edat_lfse_12)

• Persons aged 25-64 and 20-24 with upper secondary or tertiary education attainment, by sex and NUTS 2 level(%) (from 2008) (edat_lfse_13)

• Early leavers from education and training by sex and NUTS level 1 (from 2008) (edat_lfse_16)

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it “Gender Gap” in employment rates

(women & men 15-64)

• Source: “Report on equality between women and men 2010”, EC

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• Da: “ITGIRLS: Women ICT Report”; Source: “She Studies” 2006

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it Sex distribution of members of the highest decision making body of largest publicly quoted companies in 2009

• Da: “Report on equality between women and men 2010”, EC

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it Software Development

in young people

• Percentage of male and female 15 years

olds (M&F) who use the computer for

programming (2003)

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it How to attract, retain and promote talent

in your business

The HR Iris:

• Recruitment

• Retention

• Release

from: “Break gender stereotypes, give talent a chance. Toolkit ‘breaking gender stereotypes –building good business’. Tips and tools for smart Managers”, pp.1-84, Luxembourg, 2009;

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Antonio Candiello, Agostino Cortesi

DAIS – Dipartimento di Scienze Ambientali, Informatica e Statistica,Università “Ca’ Foscari” di Venezia

email [email protected]

Thanks for your attention

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