CUSTOMER SERVICE. ACCOUNTABILTY. EFFICIENCY. SECURITY.
GIS STEERING COMMITTEE
7/18/2018
Hosted by the Office of the Chief Technology Officer
- pen.data@dc.gov
GIS STEERING COMMITTEE 7/18/2018 Hosted by the Office of the Chief - - PowerPoint PPT Presentation
CUSTOMER SERVICE. ACCOUNTABILTY. EFFICIENCY. SECURITY. GIS STEERING COMMITTEE 7/18/2018 Hosted by the Office of the Chief Technology Officer open.data@dc.gov CUSTOMER SERVICE. ACCOUNTABILTY. EFFICIENCY. SECURITY. AGENDA Welcome, Data Program
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Hosted by the Office of the Chief Technology Officer
CUSTOMER SERVICE. ACCOUNTABILTY. EFFICIENCY. SECURITY.
Matthew Crossett, Office of the Chief Technology Officer
David Jackson, Office of the Chief Technology Officer
Julie Kanzler, Office of the Chief Technology Officer
David Kaehler, Department of Public Works
Mario Field, Office of the Chief Technology Officer
CUSTOMER SERVICE. ACCOUNTABILTY. EFFICIENCY. SECURITY.
CUSTOMER SERVICE. ACCOUNTABILTY. EFFICIENCY. SECURITY. Point Cloud Intensity Digital Surface Model Point Cloud Raw data via Free Viewer of 200 I ST SE Audi Field – DC United Stadium
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July 2nd and 7th
http://octo.in.dc.gov/node/165505
Desktop), Web Viewer and in ArcGIS Online marketplace (join Imagery Apps group)
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Server
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MIGRATION OF AGS INFRASTRUCTURE/MAPS2 TO 10.6
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mural project (15 MBSYEP Interns)
DCHR before moving forward with scheduling Pro classes.
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mapping)
Want to know more? Attend! Tomorrow - contact Eva Reid for info
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CUSTOMER SERVICE. ACCOUNTABILTY. EFFICIENCY. SECURITY.
CUSTOMER SERVICE. ACCOUNTABILTY. EFFICIENCY. SECURITY.
DCHA, DMV
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Local Databases)
Homes, Halfway Houses)
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Category Number of Records Example
Add Line 164
Named Alleys, Totten Mews, City Center, Georgetown University, The Wharf, Navy Yard,Fort Lincoln
Change Attribute 34
Updates Street Ranges, Street Name, Line Classification
Delete Line 47
DC Village, Audi Field, area, Military, Fort Totten
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Category Number of Records Example Add Address Tens of Thousands
Change Address 332,000
Delete Address 319
9000)
Non Residential
work on these.
Add Group Quarter 118
CUSTOMER SERVICE. ACCOUNTABILTY. EFFICIENCY. SECURITY.
CUSTOMER SERVICE. ACCOUNTABILTY. EFFICIENCY. SECURITY.
Program Manager, Data APIs & Systems
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ETL
Ingest and format for Search Engine
Search Engine
Analyzers Tokenizers Filters
Tools
Online Search Tool Desktop Geocoder
MAR DB
System of reference MAR Maintenance Tool
Images from https://thenounproject.com
API
RESTful json/geojson On API Gateway
CUSTOMER SERVICE. ACCOUNTABILTY. EFFICIENCY. SECURITY.
ETL
Ingest and format for Search Engine
Search Engine
Analyzers Tokenizers Filters
Tools
Online Search Tool Desktop Geocoder
MAR DB
System of reference MAR Maintenance Tool
Images from https://thenounproject.com
API
RESTful json/geojson On API Gateway
CUSTOMER SERVICE. ACCOUNTABILTY. EFFICIENCY. SECURITY.
CUSTOMER SERVICE. ACCOUNTABILTY. EFFICIENCY. SECURITY.
baselines exist
ValidateAddress
returns points)
example # in unit addresses)
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CUSTOMER SERVICE. ACCOUNTABILTY. EFFICIENCY. SECURITY.
CUSTOMER SERVICE. ACCOUNTABILTY. EFFICIENCY. SECURITY.
CUSTOMER SERVICE. ACCOUNTABILTY. EFFICIENCY. SECURITY.
GIS Use Case s & Ne e de d Pr
Bo th Cur r e nt and F utur e Co nside r atio n
Stre e t a nd Alle y Cle a ning Re sid e ntia l T
ra sh a nd Re c yc ling Co lle c tio n
Gro und s Ma inte na nc e Curb sid e L
e a f Co lle c tio n
Ve hic le -b a se d – e .g . Co lle c tio ns
Ma nua l L a b o r – e .g . Stre e t a nd Alle y Cle a ning
Re c o rd ing b lo c ks/ site s a s the y a re c o mple te d Re c o rd ing b e fo re a nd a fte r pic ture s/ vid e o Re c o rd ing se rvic e re q ue sts o r a ppo intme nts
tha t a re c lo se d o ut
Supe rviso r sig n-o ff QA/ QC ve rific a tio n
Ma na g e ria l kno wle d g e Supe rviso ry Co ntro l De plo yme nt Ad justme nts Co mmunic a tio n to Pub lic Pro o f o f Wo rk Co mple te d QA/ QC a nd T
ime ly Co rre c tive Ac tio ns
AVL Mobile De vic e Applic a tions Pic ture s/ Vide o
Stre ng ths
T ra c ks whe re ve hic le s g o a nd Ro ute Co mple tio n re po rting is use ful fo r ve hic le -b a se d
Co lle c ts spe c ific d a ta a b o ut a c tivitie s a nd re sults. Da ta c a n b e e nte re d into a d a ta b a se a nd c a n b e q ue rie d Pro vid e s visua l pro o f o f situa tio ns a t a spe c ific time a nd lo c a tio n
L imita tio ns
Do e sn’ t a lwa ys pro ve wo rk wa s d o ne a t tho se lo c a tio ns a nd c usto mize d usa g e
d a ta is ne e d e d . Re q uire s spe c ific fie ld wo rke r input
a ssig ne d ta sks. T ra ining , suppo rt, a nd me nto ring is ne e d e d . Inc o nsiste nt a d o ptio n, wire le ss sig na l d e a d zo ne s impe d e suc c e ssful imple me nta tio n Pic ture s d o n’ t a lwa ys sho w e xa c tly wha t ne e d s to b e sho wn a nd pro vid e limite d d a ta fo r a d a ta b a se o utsid e
a nd time sta mp
T ra c king T
imita tio ns
Sc he d ule s a ssig ne d b y a re a o r b lo c k Re a l time sta tus d a shb o a rd s GIS ma pping o f site s a nd b lo c ks to sho w sta tus
g e o g ra phic a lly
Pic ture s a sso c ia te d with X, Y c o o rd ina te s to
sho w c le a re d stre e ts a nd mo we d site s
Ma ps a nd a e ria l pho to s to sho w spe c ific site s
GROUNDS MAI NT E NANCE – F I E L D DAT A E NT RY T O RE AL
I ME DASHBOARD ST AT US F OR MANAGE RS I N T HE OF F I CE 34
2017 Mo wing Se a so n (450 site s) 2018 Mo wing Se a so n (630 site s- a s o f 3/ 13/ 18)
Auto ma te d Ima g e ry Ca pture (e .g . Ma pilla ry) to pro vid e
situa tio na l d o c ume nta tio n a t pla c e a nd time o f o ur c ho o sing with minima l impa c t o n c re ws a nd supe rviso rs
F
ie ld Ac c e ss, Dispa tc h, a nd Clo se o ut o f Se rvic e Re q ue sts with suppo rting pic ture s
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MARI O F I E L D
Pro g ra m Manager, Da ta Cura tio n
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Review other jurisdictions New York State New York City Chicago Seattle San Francisco Maryland Findings New York State has the most comprehensive documentation (roles, responsibilities, processes, dataset rules, etc.) Others provided more overall/general processes and steps.
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Initiation: Remedy Ticket Consultation: Requirement Doc and Database Report Curation: QA/QC, Data Transformation, Metadata, Testing Publication: Data Availability based on Classification Level
process, generally documenting dataset information.
customer to define the dataset requirements and specifications. Requirements document defines what the data is and curation process.
in the requirements document and processes the data. Data goes through QA/QC, documentation, testing, and final ETL development.
the appropriate platforms following the dataset classification level.
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CUSTOMER SERVICE. ACCOUNTABILTY. EFFICIENCY. SECURITY.
CUSTOMER SERVICE. ACCOUNTABILTY. EFFICIENCY. SECURITY.
CUSTOMER SERVICE. ACCOUNTABILTY. EFFICIENCY. SECURITY.
CUSTOMER SERVICE. ACCOUNTABILTY. EFFICIENCY. SECURITY.