Big Cities, Big Data, Big Lessons! - Data Across Sectors .
Big Cities, Big Data, Big Lessons!Leveraging Multi-Sector Data in Public Health toAddress Social Determinants of HealthDecember 13, 20171
Data Across Sectors for Health (DASH) DASH, a national program of the Robert Wood Johnson Foundation, waslaunched to align health care, public health, and other sectors to compile,share, and use data to address social determinants of health. DASH awarded 10 grants totaling 2 million to support projects thatimprove community health through multi-sector data sharing collaborations. DASH is a founding partner for a national peer learning network, All In: Datafor Community Health, which includes representatives from over 60community projects from around the country.
10 DASH granteesPublic Health - Seattle& King CountyWhite EarthReservation TribalCouncilCenter for HealthCare ServicesLutheran SocialService ofMinnesotaParkland Center forClinical InnovationAllegheny CountyHealth Dept.Chicago Dept. ofPublic HealthBaltimore CityHealth Dept.HealthInfoNetNYC Dept. of MentalHealth and Hygiene
60 All In Communities
Core components of DASH and All InCollaborativePartnersMulti-sectorApproachData andInformation SharingOutcomes:Increased localcapacity to drivecommunity healthImprovement
SpeakersCarrie Hoff,Deputy Director, Health &Human Services Agency, SanDiego CountyKevin Konty, MS, Director,Research and Analytics, NYCDepartment of Health andMental HygieneAmy Laurent, MSPH,Epidemiologist III, PublicHealth, Seattle & King CountyKaren Hacker, MD, MPH,Director, Allegheny CountyHealth DepartmentDarcy Phelan-Emrick, DrPH,Chief Epidemiologist,Baltimore City HealthDepartment
Neighborhood Tabulation Areas: Enhancing populationhealth improvement capacity in NYC through sharedinformation at the small area levelKevin KontyNew York City Department of Health and Mental HygieneBig Cities, Big Data, Big Lessons!DASH-APHA WebinarDecember 13th, 20177
Neighborhood Tabulation Area ProjectObjective: to work with partners to bringtogether health and social determinants ofhealth data at the neighborhood-level using anew geographic scale, the NeighborhoodTabulation Area (or NTA).NTAsCount 188Median Population 36,6008
PartnersCity Agencies New York City Department of Health and Mental Hygiene (DOHMH) Department of City Planning (DCP) Center for Innovation through Data Intelligence (CIDI) Department of Correction (DOC) Department for the Aging (DFTA) Department of Social Services (DSS) Department of Homeless Services (DHS) Human Resources Administration (HRA)Organizations The New York Academy of Medicine (NYAM) United Hospital Fund of New York (UHF) The Fund for Public Health in New York City (FPHNYC)9
NTA Project Motivation Increased focus onSocial Determinants ofHealth (SDOH) Health data often lackSDOH information Necessity of linkinghealth with census andother data at censusgeography Optimal censusgeography forneighborhood health?Source: es/topic/socialdeterminants-of-health10
Neighborhood Defined as Community District(CD) 59 CDs in NYC Benefits of CD:o Critical geography for communityplanning and decision makingo Each CD approximates a PublicUse Microdata Area (PUMA):readily available census datao Example: Community HealthProfiles 2015 Limitation of CD: medianpopulation of 140,000 maymask potential heterogeneityCDsCount 59Median Population 140,00011
Neighborhood Tabulation Area (NTA) Statistical area created byDepartment of CityPlanning NTA is aggregation ofcensus tracts within thesame PUMA “Minimum” population of15,000 A useful geography forassessing and analyzingneighborhood healthNTAsCount 188Median Population 36,60012
Desirable Properties of Geography forNeighborhood Health Assessment and Analysis GranularityReliabilityCorrespondence to neighborhood boundariesSpatial congruityTemporal consistencyCompared with other geographies withavailable census data (CD, census tract, ZIPCode), NTAs generally represent the besttradeoff among these desirable attributes13
NTA is more granular than CD (PUMA)14
NTA is more granular than CD (PUMA)15
Age-adjusted Premature Mortality Rate in CD 313(Brighton Beach & Coney Island)CD 313 218 per100,00016
NTA estimates are more reliable than CTestimates17
Unlike zip codes, NTAs correspond tohistorical neighborhood boundariesNTAs have identifiable neighborhood namesManhattanN RiverdaleFieldstonRiverdaleSpuyten wldCo-Op CityBaychstrNorwoodV Cortlandt VlgBronxMarble HlBedfrd PkInwoodFordhmKingsbridgeHts N BronxdaleAllertonPelham GdnsFordhmBelmontSWash Hts NPelham PkwyUniversity HtsMorris HtsVan NestMt HopeMorris Pk Pelham BayE TremontWestchtr Sq Country ClubClarmntCity IslandBathgateWash Hts SParkchstrW FarmsE ConcBronx Riv WestchstrConc VlgUnionprtMorrisaniaSoundviewHamilton HtsMelroseBrucknerSchuylervilleThrogs NckSoundview Edgewater PkManhattanvilleMelroseLongwoodSCastle HlMott Haven NClason PtHarding PkHunts PtMott HavenMorningside HtsPrt MorrisCentral Hrlm SE Hrlm NUpper W SideRikers IslandE Hrlm SSteinwayYorkvilleLincoln SqUpper E SideCarnegie HlOld AstoriaWest College PtFt TottenBay TerClearvwQueensMurray HlLenox HlRoosevelt IsClintonDouglas tBayside HlsLittle NckMidtown SJackson HtsQueensbridgeTurtle BayRavenswoodE FlushingE MidtownN CoronaLICHudson YrdsChelseaGlen OaksFlat IronFloral PkWoodside ElmhurstMurray HlAuburndaleUnion SqNew Hyde PkQueensboro HlKips BayOakland GdnsHunters PtCoronaSunnysideElmhurstW MaspethMaspethGramercyFreshMdwsBellerosePomonokW VlgStuy TownFlushing HtsUtopiaCooper Vlg GreenpointHillcrestRego PkE VlgMaspethKew Gdns HlsSoHoTriBeCaForest HlsJamaica EstsCivic CtrMiddle VlgHolliswoodLittle Italy Lower E SideN SideQueens VlgChinatownS SideE WilliamsburgBriarwoodHollisJamaica HlsBattery Pk CityKew GdnsLower MN DUMBORidgewoodJamaicaVinegar HlGlendaleDwntwn BK WilliamsburgBoerum HlBK HtsRichmond HlS JamaicaCambria HtsCobble HlBedfordSt AlbansFt GreeneWoodhavenStuyvesant HtsCypress HlsClinton HlCity LineCarroll GdnsBaisley PkColumbia StProspect HtsOcean HlOzone PkS Ozone PkLaureltonRed HookCrown Hts NPk SlopeSpringfield Gdns NGowanusCrown Hts SE New York (PA Ave)BrownsvilleSpringfield Gdns SBrookvilleProspect Lffrts GdnsE New YorkLindenwoodWingateRosedaleHoward BchRugbyWindsorTerRemsen VlgSunset Pk WStarrett CityJOHN F. KENNEDYINTERNATIONALAIRPORTErasmusFlatbushSunset Pk EKensingtonOcean PkwyE FlatbushFarragutCanarsieBrooklynW New BrightonNew BrightonSt GeorgeMariner's HbrArlingtonPrt IvoryGranitevilleNew BrightonSilver LakeBorough PkBay RdgWesterleighGrymes HlCliftonFox HlsStapletonRosebankNew ocharFt WadsworthOld TownDongan HlsS BchTodt HlEmerson HlHeartlnd VlgLghthouse HlNew DorpMidland BchOakwoodOakwood BchArden HtsGreat KillsFlatlandsDyker HtsMidwoodOcean Pkwy SBensonhurst WBath BchGrgtwnMarine PkBergen BchMill BasinMadisonFar RckwyBayswaterBensonhurst EHomecrestGravesendSheepshead BayGerritsen BchMN BchHammelsArverneEdgemereBrighton BchW BrightonSeagateConey IsBreezy PtBelle HbrRckwy PkBroad ChannelRossvilleWoodrowNeighborhood Tabulation Areas or NTAs, areaggregations of census tracts that are subsets ofNew York City's 55 Public Use Microdata Areas(PUMAs). Primarily due to these constraints, NTAboundaries and their associated names may notdefinitively represent neighborhoods.CharlestonRichmond VlyTottenvilleAnnadaleHuguenotPrince's BayEltingvlSource: U.S. Census Bureau, American Community Survey, 2006-2010 Summary FilePopulation Division-New York City Department of City Planning
Data Sources American Community SurveyNYC Department of Health and Mental Hygiene––––Vital StatisticsDisease ControlEnvironmental HealthA1C RegistryOther city agencies–––––Administration for Children’s ServicesDepartment of Social Services Human Resources Administration Department of Homeless ServicesDepartment for the AgingDepartment of CorrectionDepartment of Education (YC FITNESSGRAM)ED/hospitalizations claims database–Statewide Planning and Research Cooperative System (SPARCS) NYC Medicaid dataHealth Data NYNYC Open Data 100 indicators have been created and linked using the above data toassess social determinants of health19
Key Project Activities Inclusion of 100 indicatorsAutomated geocoding routineDOHMH NTA population estimatesData DisseminationDevelopment of use cases20
Data Uses Identify health concerns and disparities at the neighborhoodscaleo Targeting, surveillance, evaluationo Pockets of high burden areas outside of Neighborhood Health ActionCenter neighborhoods Uncover social determinants of heath in communitieso Premature mortality and jail incarcerationo Legionnaires’ disease and cooling tower density Emergency Preparedness Help drive community prevention planning and investmentso TCNY Neighborhood Health Initiative investmentso IMAGE-NYC (interactive map of aging in NYC)o UHF Medicaid Institute report(s)21
Potential Uses Long-term cross-agency surveillance and reportingo Expansion to other agencieso Systemization of initial efforts Hierarchical/multi-level modeling effortso Neighborhood context Ecological cost exercises Long term planningo NTAs were constructed for long term population projections Increased cooperation/coordinationo Between agencieso With Community-Based Organizationso With the public22
Conclusions NTAs represent a useful geography toorganize NYC data to examine and promoteneighborhood health Issues with incorporating survey data suchas Community Health Survey representpotential limitation23
Acknowledgements Funding for this project is provided by RWJF Data AcrossSectors for Health (DASH) The project was led by Tsu-Yu Tsao and the Office of PolicyPlanning and Strategic Data Use Special thanks to the Department of City Planning and theCenter for Innovation through Data Intelligence whoplayed (and will play) key roles in the success of the project. Please contact Tsu-Yu Tsao with questions and suggestions:ttsao@health.nyc.gov or me kkonty@health.nyc.gov24
Thank You25
Allegheny County Data SharingAlliance for Health (ACDSAH)Public health, Human services, Economic development,Health care and TransportationVision: a connected data warehouse that provides multi-source datafor cross sector decision making to impact the health of the 130municipalities and 1.2 million residents in Allegheny County.
Allegheny CountyPercent Below Poverty Level 2012Source: US Census Bureau
Stakeholders/Partners IntergovernmentalHuman Services, EconomicDevelopment, CountyStat Managed Care OrganizationsUPMC, Gateway, Highmark Advisory Coalition for ACHD Local organizationsJewish Healthcare Foundation,Traffic 21, RAND, University centerfor social and urban research, PublicHealth Dynamics Laboratory,American Heart Association,American Diabetes Association
Allegheny County Data Sharing Alliancefor Health (ACDSAH)Socioeconomic dataGOALS:Exemptions to smoking banClaims data To merge existing cross-sectordata sets for decision making To understand the risk ofcardiovascular mortality acrossAllegheny County To integrate data into a singleaccurate model (FRED)to assessimpact of social determinantsBuilt environmentFoodAccessEnvironmental health data
DASH Data WarehouseHealth Inputs-Obesity rates-Smoking rates-Medical claims dataHypertensionDiabetesHyperlipidemiaDiagnosed & Diagnosed MedsCo-morbidityHypertension Diabetes Hyperlipidemia (diagnosed)Anxiety medicationDepression medicationNatural Environment-Air QualityTRIPM 2.5-Land UseWoodlands/ forestGreenwaysBarren LandSocial-DemographicsAgeRaceGenderMedian incomePoverty ratesEmployment RatesEducational attainment-Access to TransportationVehicle OwnershipCommute time to work-Homicide-Age of DeathBuilt Environment-Land useRoadwaysParksTrailsAgriculture landUrban-Traffic Data911 response timeHourly Traffic Counts-Health facilitiesPrimary CareHospitals-Vacant properties-Home ownership/ rentals-Age of housing-Walk Scores-Illegal dump Sites-Food AccessFast foodFarmers marketsSupermarkets-Food deserts-Tobacco vendors-Alcohol vendors-Exempt clean air vendors
MCO Data
FREDFramework for Reconstructing Epidemiologic nterventionsFRED is an open-source, agent-based modeling platform developed by thePublic Health Dynamics Laboratory at University of Pittsburgh GraduateSchool of Public HealthGrefenstette JJ, Brown ST, Rosenfeld R, et al. FRED (A Framework for Reconstructing Epidemic Dynamics): An open-sourcesoftware system for modeling infectious diseases and control strategies using census-based populations. BMC Public Health, 2013Oct;13(1), 940.DASH - FRED33
Controlling for “expected” riskActual MortalityPredicted Risk-Expected-Observed Lower than expected deathsHigher than expected deaths“difference” – larger negative numbers are worseDASH - FRED34
Difference BetweenObserved and ExpectedRisk by Census TractFood StampsObesityPercent of housing in poor conditionPercent vacant housingDiabetesHypertensionDiabetes and hypertensionModeled CVD MortalityRisk With 40% Reductionin all SDOH
Top Lessons Learned Data on the direct impact of socialdeterminants on CVD is lacking Getting all major insurers involved is criticalfor coverage It is difficult to get agreement on a singleintervention-so allow for independence
Next Steps Strategize with partners possibleinterventions Refocus on another outcome-asthma, opioidoverdoses Continue to refine FRED Sustain data
Baltimore Falls ReductionInitiative EngagingNeighborhoods and Data(B’FRIEND)Darcy Phelan-Emrick, DrPH, MHSDecember 13, 2017First presented at APHA Session 3157.0 on November 6, 2017Leana Wen, M.D., M.Sc.Commissioner of Health, Baltimore CityCatherine E. PughMayor, Baltimore City@Bmore ity.gov38
Presenter DisclosuresDarcy Phelan-EmrickThe following personal financial relationshipswith commercial interests relevant to thispresentation existed during the past 12months:No relationships to discloseCatherine E. PughMayor, Baltimore CityLeana S. Wen, M.D., M.Sc.Commissioner of Health, Baltimore City39
BackgroundIn 2015, over 3 million older adults weretreated for falls in emergency departments(EDs) in the US1Effective falls prevention includes exercise,home modification, vision screening, etc.Health information exchanges (HIEs) canbe leveraged for public health use cases,including surveillance2Catherine E. PughMayor, Baltimore CityLeana S. Wen, M.D., M.Sc.Commissioner of Health, Baltimore CityWISQARS, 2015, non-fatal injury query forunintentional falls among 65 years, NEISS AllInjury Program, accessed 10/30/2017;2 PMC3052326140
Sectors Involved Maryland’s HIE, CRISP (ChesapeakeRegional Information System for OurPatients) Baltimore City Housing Baltimore City 311 System (citizenrequests for service) Social service providers Hospitals Academic institutionsCatherine E. PughMayor, Baltimore CityLeana S. Wen, M.D., M.Sc.Commissioner of Health, Baltimore City41
B’FRIEND GoalB’FRIEND is a collaboration between theBaltimore City Health Department, CRISP,and many partnersFunding for infrastructure provided byRWJF DASH (ID 73348)Goal: To decrease the rate of falls leading toan ED visit or hospitalization among olderadults (65 years) by one-third in threeyears in Baltimore City, MarylandCatherine E. PughMayor, Baltimore CityLeana S. Wen, M.D., M.Sc.Commissioner of Health, Baltimore City42
MethodsSurveillance population: Older adultresidents (65 years) of Baltimore CityTimeframe: October 2015 – PresentData source: Maryland Health Services CostReview Commission (ED and hospitalizationcase-mix data with CRISP unique identifier)Outcome: Falls-related ED visits andhospitalizations identified by ICD codes3Catherine E. PughMayor, Baltimore CityLeana S. Wen, M.D., M.Sc.Commissioner of Health, Baltimore CityConsensus Recommendations For Surveillance ofFalls and Fall-Related Injuries, Injury SurveillanceWorkgroup on Falls (ISW4), 2006343
ZIP code 21211Number of falls-related ED visits andhospitalizations among older adults bymonth, Oct 2015 – Aug 2017 10Catherine E. PughMayor, Baltimore CityLeana S. Wen, M.D., M.Sc.Commissioner of Health, Baltimore CityData source: Maryland HSCRC Inpatientand Outpatient Case Mix Data with CRISPEID since October 201544
ZIP code 21211Percent sex and percent race of fallsrelated ED visits and hospitalizationsamong older adults, Oct 2015 – Aug 2017SexRace31%69%FemaleCatherine E. PughMayor, Baltimore CityLeana S. Wen, M.D., M.Sc.Commissioner of Health, Baltimore CityMaleData source: Maryland HSCRC Inpatientand Outpatient Case Mix Data with CRISPEID since October 201545
ZIP code 21211Number of falls-related ED visits andhospitalizations among older adults byage group , Oct 2015 – Aug 2017Catherine E. PughMayor, Baltimore CityLeana S. Wen, M.D., M.Sc.Commissioner of Health, Baltimore CityData source: Maryland HSCRC Inpatientand Outpatient Case Mix Data with CRISPEID since October 201546
ZIP code 21211Percent for number of visits per patient forfalls-related ED visits and hospitalizationsamong older adults, Oct 2015 – Aug 2017Interpretation:Among those withfalls-related EDvisits andhospitalizations inthis ZIP code, about17% had 2 fallsrelated ED visitsand hospitalizationsduring the timeperiodCatherine E. PughMayor, Baltimore CityLeana S. Wen, M.D., M.Sc.Commissioner of Health, Baltimore CityNumber offalls-relatedED visits andhospitalizationsper patientData source: Maryland HSCRC Inpatientand Outpatient Case Mix Data with CRISPEID since October 201547
Lessons LearnedWorking across sectors can be more difficultthan one expectsLocal government bureaucracy and politicspresent notable challenges to innovationContractingChanges in elected/appointed leadersLegal agreementsLocal and meaningful data excite partnersand create momentum for real change!Catherine E. PughMayor, Baltimore CityLeana S. Wen, M.D., M.Sc.Commissioner of Health, Baltimore City48
Next StepsContinue using B’FRIEND for surveillanceand targeting falls prevention activitiesIncorporate additional data from sourcessuch as EMS calls for service,transportation, older adult home visitingprograms, weather, etc.Conduct further epidemiologic andgeospatial analyses (“hot spots”)Catherine E. PughMayor, Baltimore CityLeana S. Wen, M.D., M.Sc.Commissioner of Health, Baltimore City49
King County Data Across Sectorsfor Housing and HealthAmy Laurent, Epidemiologist
BackgroundPartnersProject GoalsResultsLessonsNext StepsAcknowledgementsLife expectancy in KingCounty by census tractvaries by 24 years
BackgroundPartnersProject GoalsResultsLessonsNext StepsAcknowledgements
BackgroundPartnersProject GoalsResultsLessonsNext StepsAcknowledgementsTo help public housing authorities have a better understanding of the healthconditions of their population; enable program and policy development andevaluation Task 1: Link Medicaid claims data with PHA resident data Medicaid claims hold the information from a medical encounter with a provider (doctor, hospital,procedure, prescription) PHA resident data from the Moving To Work (MTW) 50058 form Task 2: Provide PHAs a de-identified dataset and visualizations with codedhealth conditions for enhanced in-house ability for assessment andevaluation Task 3: Sustain this process for regular exchangeAllows PHAs to take a deeper dive into the data and start to answerquestions that previous static linkages have raised.53
BackgroundPartnersProject GoalsResultsLessonsNext StepsAcknowledgementsShort termIntermediateLong termPHAs gain understandingabout healthIncrease datasets being linkedDecreased health inequitiesPH gains understandingabout housingUse for program planning andevaluationPotential for care coordinationIntegrated system for regularand routine linkageShare programming acrossACHsReturn on investmentHealth status of PHA residentreportElucidation of housing-healthrelationshipsTriple AimParticipation in King CountyAccountable Community ofHealthPartnership structure to buildon for other cross-sector work55
BackgroundPartnersProject GoalsRaw KCHA files2004-2016ResultsRaw SHA files2004-2016Link, append, and reshapeLessonsNext StepsAcknowledgementsRaw Medicaid files2012-2016Link and appenddata1KCHA35,377 households94,932 individualsAlign formats, appendSHA data138,084 households85,986 individualsPHA data63,671 households149,401 individuals361,037 recordsRestructure to have start and end date864,843 individuals21,150,021 recordsRestrict to most recent data for each individualDeduplication, remove those who exited prior to 2012Inner join on SSN103,494 individuals103,494 records764,207 individuals2764,207 records88,351 individuals89,289 records1 Households identified by unique HH SSN Individuals identified by unique combos of SSN and DOB for both PHAs2 Defined as a unique Medicaid ID and SSN combo
BackgroundPartnersProject GoalsResultsLessonsPHA and Medicaid enrollment over timeNext StepsAcknowledgements
BackgroundPartnersProject GoalsResultsLessonsNext StepsMaps to identify enrollment opportunitiesAcknowledgements
BackgroundPartnersProject GoalsResultsLessonsNext StepsAcknowledgements Data are under review before release PHAs serve a Medicaid population with higher rates of Chronic disease Injury Adult asthma We see different distributions of disease and opportunities forprogramming across the PHAs Avoidable ED use remains off target Rates of prevalence computed using claims fall below the generalpopulation measures for many chronic diseases There may be room for improvement on enrollment into Medicaid
BackgroundPartnersProject GoalsResultsLessonsNext StepsAcknowledgements Bringing the right data people to the table is essential The importance of partnering and discussion can’t be dismissed Housing tends to look at their analysis units at the household level; public healthat an individual Large datasets require a lot of clean up and discourse, even when using“standardized” data DSA among the PHAs When possible, fund the partnerto do to their data work Valuable insights from the data Opportunities for partners to drilldown into their data Complexities in working with claims data
BackgroundPartnersProject GoalsResultsLessonsNext StepsAcknowledgements Continued analytics Share code for processing the HUD 50058 form Non-federally funded low-income housing data Identified Medicare data Refine code and continue to make publically available via Github Revisit the data extract from PHA; perhaps non-50058information may be helpful for data accuracy
BackgroundPartnersProject GoalsResultsLessons Project funded by RWJF Illinois Public Health Institute and theMichigan Public Health Institute Washington State Health Care Authority Partners: Sarah Oppenheimer and AlexisWarth from KCHA and Denille Bezemer andKate Allen from SHA; Betsy Lieberman Superstar PH Analysts: Alastair Matheson,Lin SongNext StepsAcknowledgements
Questions?Carrie Hoff,Deputy Director, Health &Human Services Agency, SanDiego CountyKevin Konty, MS, Director,Research and Analytics, NYCDepartment of Health andMental HygieneAmy Laurent, MSPH,Epidemiologist III, PublicHealth, Seattle & King CountyKaren Hacker, MD, MPH,Director, Allegheny CountyHealth DepartmentDarcy Phelan-Emrick, DrPH,Chief Epidemiologist,Baltimore City HealthDepartment
Keep in Touch and Join the NetworkConnect with DASH Visit our website: dashconnect.org Follow @DASH connect on TwitterConnect with All In: Data for Community Health Visit our website: allindata.org Join the online virtual learning community! allin.healthdoers.org Subscribe to the All In newsletter Follow #AllInData4Health on TwitterUpcoming Webinars: dashconnect.org/calendar
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A. The Smarter Cities Challenge In 2010, IBM Corporate Citizenship launched the Smarter Cities Challenge to help 100 cities around the world over a three-year period become smarter through grants of IBM talent. Boston, Massachusetts, was selected through a competitive process as one of 33 cities to be awarded a Smarter Cities Challenge grant in .
Cities are moving from pledges to action, with 27 of the world's largest cities in the C40 Cities Initiative having achieved at least 10 percent lower emissions than their peak as they work towards decarbonization by 2050. Through this initiative, cities are starting to develop Paris Agreement Compatible Climate Action Plans. Seven C40 cities .
The process of analyzing big data to extract useful information and insights is usually referred to as big data analytics or big data valu e chain [6], which is considered as one of the key enabling technologies of smart cities [7, 8, 9]. However, big data complexities comprise non-trivial challenges for the processes of big data analytics [3].
Smart. cities Europe.connected In this context, the digitalisation of cities, underpinned by emerging technologies such as 5G, IoT, edge computing and the further application of Big Data solutions will be key to the transformation of cities to
The Rise of Big Data Options 25 Beyond Hadoop 27 With Choice Come Decisions 28 ftoc 23 October 2012; 12:36:54 v. . Gauging Success 35 Chapter 5 Big Data Sources.37 Hunting for Data 38 Setting the Goal 39 Big Data Sources Growing 40 Diving Deeper into Big Data Sources 42 A Wealth of Public Information 43 Getting Started with Big Data .
to technology suppliers to Smart Cities, Smart City program managers, academics researching innovation and Smart Cities, and disability organizations and advocates working to make innovation and Smart Cities more inclusive. This Playbook has been designed to complement other tools that make up the . Smart Cities for All . Toolkit.
State Tax Sharing with Cities: Premier Type Tourist Resort Cities as Models the likelihood that other cities could develop thriving tourism industries with the same sort of assistance. Given that these funds have been an effective means of increasing tourism and, thus, state tax dollars, a program that can benefit more cities has the
The Baldrige framework is used extensively as a foundation for internal systems, but there has been a substantial decrease in the number of manufacturing organizations applying for the award. This research study validates some of the reasons associated with that development. The Value of Using the Baldrige Performance Excellence Framework in Manufacturing Organizations Prabir Kumar .
