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Prostorska poizvedba ESRI ST_Geometry traja predolgo

Prostorska poizvedba ESRI ST_Geometry traja predolgo


V svoji aplikaciji uporabljam ESRI ST_Geometry in ugotovil sem, da je prostorska poizvedba precej počasna:

Naslednji sql se uporablja za poizvedbe pois znotraj določenega bounding_box:

izberite * iz (izberite ime, st_astext (oblika) kot obliko iz POIView h, kjer je 1 = 1, in ST_Intersects (h.shape, st_geometry ('POLYGON ((20.227742 30.1681829, 20.296578 30.168182, 20.296578 30.21127,20.22774 30.211270.1 20.22774 30.1 , 3)) = 1) kjer je rownum <= 10

Preštejte skupna števila:

izberite število (obliko) iz POIView h, kjer je 1 = 1 in ST_Intersects (h.shape, st_geometry ('POLYGON ((20.227742 30.1681829, 20.296578 30.168182, 20.296578 30.21127,20.22774 30.211270, 20.22774 30.16818))', 3)

In prvi sql bo stal skoraj 3 sekunde, drugi pa 4+ minute, kar je nesprejemljivo.

Še več, bounding_box je precej preprost poligon z majhno površino, ne morem si predstavljati, kako dolgo bo stalo, ko bom za argument postavil zapleten poligon.

Ali je to mogoče popraviti?

BTW pa vse tabele ArcMap 10.0 s povezavo sde izvozi v Oracle 11g in v tabeli imamo le 120.000 zapisov.


Posodobitev: pogled:

Ustvari ali zamenjaj OGLED POIView (objectid, oblika, id, ime, naslov, table_name) AS SELECT objectid, shape, hotel_id kot id, hotel_name kot ime, naslov, 'HotelPoint' AS ime_tabele iz HotelPoint union ALL SELECT objectid, shape, school_id kot id, ime_šole kot ime, naslov, 'SchoolPoint' AS ime tabele iz zveze SchoolPoint ALL SELECT objectid, oblika, id, ime, naslov, 'ImportFeaturePoint' AS ime_tabele iz ImportFeaturePoint union ALL SELECT objectid, shape, id, name, address, 'ImportFeatureLine 'KOT ime_tabele iz zveze ImportFeatureLine ALL SELECT objectid, oblika, id, ime, naslov,' ImportFeaturePolygon 'AS ime_tabele iz zveze ImportFeaturePolygon VSE ...

In ko sem sledil komentarju @Vince, sem poskušal spremeniti sql v:

izberite * od (izberite ime, st_astext (oblika) kot obliko iz POIView h kjer ST_ENVINTERSECTS (h.shape, st_geometry ('POLYGON ((20.227742 30.1681829, 20.296578 30.168182, 20.296578 30.21127,20.22774 30.211270, 20.22774 30.211270, 20.22774 = 1) kjer je rownum <= 10

Zdaj traja 6 sekund. In štetje sql:

izberite število (obliko) iz POIView h, kjer je ST_ENVINTERSECTS (h.shape, st_geometry ('POLYGON ((20.227742 30.1681829, 20.296578 30.168182, 20.296578 30.21127,20.22774 30.211270, 20.22774 30.16818))', 3)

traja 240 sekund.

Prenosam sp5, nov rezultat bom objavil po namestitvi popravka.


Posodobi po namestitvi SP5 in ugotovil sem, da bo poizvedba trajala dlje kot prej, in sicer 24 sekund za osnovno poizvedbo in 2 minuti za poizvedbo štetja.


Poenostavljena shema relacijske baze podatkov za pretvorbo podatkov BIM v poizvedbeno učinkovito in prostorsko omogočeno bazo podatkov

Z naraščajočim sprejemom BIM-a v industriji AECO (arhitektura, inženiring, gradbeništvo in lastniki-operaterji) je vsebina BIM vse bolj popolna in dragocena. Na žalost so podatki večinoma zaklenjeni znotraj ustreznega BIM-ovega orodja za avtoring z omejenimi možnostmi, da lahko uporabniki izvajajo poizvedbe poleg preprostih poizvedb o predmetih in njihovih lastnostih. Tudi z uporabo odprtega formata za izmenjavo podatkov, kot je IFC (Industry Foundation Classes), pridobivanje podatkov iz BIM še vedno ni lahka naloga. Ta raziskava se osredotoča na opredelitev sheme in pravil pretvorbe, da se podatki BIM prenesejo v obliko, ki jo je enostavno povprašati. Shema je definirana z uporabo priljubljene relacijske strukture baze podatkov po uveljavljenem modelu zvezdne sheme, ki je dobro definiran v domeni podatkovnega skladišča. Primarni cilj koncepta je omogočiti prilagodljive in učinkovite poizvedbe v podatke BIM z uporabo standardnega SQL, ki odstrani omejitve vnaprej določenih poizvedb in učinkovito pretvori podatke BIM v odprto bazo podatkov, ki jo je mogoče iskati. Drug pomemben prispevek te raziskave je pomemben potencial za podporo prostorskim poizvedbam. To je kritična lastnost, ki jo v sedanjem pristopu do baz podatkov, ki temeljijo na BIM, pogosto zanemarjajo. V prispevku je predstavljena integracija prostorskih operacij v standardizirane poizvedbe SQL, ki omogočajo dostop do podatkov BIM za široko paleto zmožnosti poizvedb. Takšna funkcija bo omogočila veliko boljšo vidnost podatkov BIM za boljše procese odločanja.


Pomoč ArcMap 10.1 - pridobivanje podatkov

Žal, če je moja terminologija napačna, sem se šele učil tega programa.

Na zemljevidu imam več točk, na katerih sem ustvaril odbojnik s premerom 4 km. Zemljevid je raster z ločljivostjo 1 km (s spletnega mesta http://www.worldclim.org/) in na zemljevidu moram izvleči podatke. V bistvu želim vzeti vrednosti (na primer temperaturo), povezane z vsakim 1 kvadratnim kilometrom slikovne pike, s katero se prekriva moj vmesnik, in jo izvoziti kot datoteko .dbf, da jo lahko odprem v Excelu in naredim nadaljnjo analizo. Zdi se mi, da preprosto ne najdem načina za to.

Vsaka pomoč bi bila zelo hvaležna! Hvala!

Glede na to, da je vaš vmesnik verjetno vektorska datoteka, lahko pretvorite svoj rastr v datoteko točke, nato naredite prostorsko poizvedbo, katere točke so v vmesniku, izvozite svoj izbor kot novo plast in nato iz njega izvozite dbf. Prepričan sem, da ima nekdo boljši način.

Poskusil bom to. Moji glavni pomisleki so: 1) raster je precej velika datoteka in bo potreboval nekaj časa za pretvorbo (domnevam), kar sicer ni prevelik posel, toda 2) bo rezultat pretvorbe ena točka na kvadratni km? Če je tako, bom lahko izbral samo tiste točke, ki se zgodijo znotraj mojega vmesnega pomnilnika, in ne tiste, katerih 1 kvadratni kilometer slikovnih pik se prekriva z vmesnim pomnilnikom, vendar katerih točka ne (da & # x27 je zamotan stavek, upam, da ujamete moj pomen).

Če ste nov v ArcGIS, vedite, da obstaja veliko, veliko različnih načinov, kako priti do cilja.

Če vas prav razumem, je vaš končni cilj, da imate bazo podatkov, ki vsebuje temperaturo za vsak slikovni pik, ki leži znotraj 4k nekaterih fiksnih točk, in da te fiksne točke že imate kot datoteko oblike in 1 km razdaljo. Predvidevam, da so v isti projekciji (v nasprotnem primeru te točke znova projicirajte v isto projekcijo kot raster).

Tega problema bi pristopil takole:

Uporaba ArcGIS z razširitvijo prostorskega analitika

Z uporabo točk bi ustvaril 4 km vmesnega pomnilnika (orodja za analizo & gtProximity & gtBuffer)

Ta vmesnik bi uporabil za izločanje samo pikslov iz rastra, ki sem ga želel analizirati (Spatial Analyst Tools & gtExtraction & gtExtract by Mask)

Nato bi piksle pretvoril v točke (Orodja za pretvorbo & gtFrom Raster & gt & gtRaster v Points) (prepričajte se, da je želena rastrska vrednost dodeljena izhodu polja)

Če točke nimajo širine / dolžine ali X / Y (žal, to počnete iz pomnilnika), dodajte dva stolpca (Desna tipka miške kliknite tabelo atributov in gtopen, v prvem spustnem meniju izberite Dodaj polje, poskrbite, da naredite jih dvojne, označite jih Xcord & amp Ycord (ali Lat & amp Long. Nato izberite stolpec tako, da z desno miškino tipko kliknete glavo & gtCalculate Geometry, izberete koordinato X in ustrezno projekcijo in enoto.

Pojdite v mapo, v katero ste shranili to datoteko shape shape, odprite datoteko shape shape (razširitev .dbf) z excelom, shranite kot excel datoteko.

Tako boste dobili bazo podatkov s temperaturami v središču vsakega 1 km slikovnih pik znotraj 4 km od točk, skupaj s koordinatami za identifikacijo / analizo.

Težave s to metodo - nekatere slikovne pike bodo imele dele, ki spadajo v domet 4 km, vendar ne bodo vključeni b / c. Izvleček maske zajema samo piksle, ki so več kot 50 v vmesnem pomnilniku. Če se odločite, da potrebujete tudi te podatke, lahko namesto tega naredite naslednje, čeprav se morate sami odločiti, kaj bi morali vključiti.

Uporaba ArcGIS z razširitvijo prostorskega analitika

Z uporabo točk bi ustvaril medpomnilnik 4 km in 5 km (orodja za analizo & gtProximity & gtBuffer)

S tem 5-kilometrskim vmesnikom bi iz rastra, ki sem ga želel analizirati, izvlekel samo piksle (Spatial Analyst Tools & gtExtraction & gtExtract by Mask)

Nato bi pretvoril piksle v točke (Orodja za pretvorbo & gtFrom Raster & gt & gtRaster v Points) (prepričajte se, da je želena rastrska vrednost dodeljena izhodu polja)

Začnite urejati datoteko točke (z desno miškino tipko kliknite datoteko točke & gtEdit & gtEdit Features)

Izberite vse točke, ki jih želite obdržati

Izberite vse točke v obsegu 4 km (iz glave-orodne vrstice Izbira & gtIzberi po lokaciji in funkcije gtSelection med tem, "kar ste poimenovali točke" v "tisto, kar ste poimenovali vmesnik 4km" & gtok), bodo označeni vsi centroidi (v cyan, najverjetneje)

Preglejte preostale točke (tiste med pasovi 5 km in 4 km - če so točke, za katere menite, da jih je treba vključiti [b / c je rastrski pik v pasu 4 km, čeprav centroid ni], izberite tudi to (Z držanjem shift, ko kliknete to točko)

Ko so izbrane vse ustrezne točke, z desno miškino tipko kliknite Cat. Tabela atributov drevo in gtopen. Izberite ikono Stikalo (zdaj niso izbrane vse tiste, ki jih želite obdržati, in vse tiste, ki se jih želite znebiti, so označene, najverjetneje v cyan-u). Izbriši (ali z desno miškino tipko kliknite označeno vrstico in izberite izbriši izbrano (mislim). S tem boste odstranili katero koli od 5 km točk, ki jih ne želite na svojem študijskem območju.

Zaprite pojavno okno tabele in pojdite na spustno orodno vrstico za urejanje in izberite shrani spremembe, ustavite urejanje.

Če točke nimajo širine / dolžine ali X / Y (žal, to počnete iz pomnilnika), dodajte dva stolpca (Desna tipka miške kliknite tabelo atributov in gtopen, v prvem spustnem meniju izberite Dodaj polje, poskrbite, da naredite jih dvojne, označite jih Xcord & amp Ycord (ali Lat & amp Long. Nato izberite Colum z desnim klikom na glavo & gtCalculate Geometry, izberite X koordinata in primerno projekcijo in enoto.

Pojdite v mapo, v katero ste shranili to datoteko shape shape, odprite datoteko shape shape (pripona .dbf) z excelom, shranite kot excel datoteko.

In tu greš ... To predvideva, da sem zahtevo pravilno razumel - potem obstaja nekaj možnosti, kako pristopiti, toda kot sem rekel, obstaja veliko različnih načinov, kako priti tja, kam greš ...

Če to ni tisto, za čimer ste, upam, da vam bo nekaj teh majhnih orodij pomagalo.


Prostorska poizvedba ESRI ST_Geometry traja predolgo - Geografski informacijski sistemi

V mreži ima vsak piksel svojo značilnost. To je lahko koda, lahko pa tudi številka, na primer vodostaj ali topografski nivo. Ko se mreže primerjajo na istem mestu, postane enostavno izračun med mrežami.

Oglejmo si primer. Na sliki 14.1 je prikazan prerez poplavnih ravnic v Pais Pesci z rjavo črto, ki predstavlja topografijo kopnega, modra črta pa gladino vode.

SLIKA 14.1
Prerez poplavne ravnice Pais Pesca

V prerezu je modra črta površinska ploskev, ustvarjena za vodostaje (kot tista, ki ste jo naredili prej) in takoj vidite, da je voda tam, kjer je topo-nivo (rjava črta) nižji od površinske ploskve . Za izdelavo zemljevida poplav, to je dejanskega obsega poplav, potrebujete ustvarjeno mrežo vodostaja in mrežo, ki predstavlja topografijo poplavnih ravnic.

Dve datoteki mreže imata vrednosti, priložene vsakemu pikslu. To nam omogoča, da izračunamo globino vode na vsaki lokaciji, ki jo predstavlja slikovna pika, tako da preprosto odštejemo vrednost topo-nivoja od vodostaja.

Globina vode = Raven vode-Raven Topo

Primer tega izračuna je predstavljen v tabeli 14.1 in jasno je, da ko je globina vode negativna, je območje suho.

TABELA 14.1
Primer izračuna globine vode v poplavnem območju Pais Pesca

Nivo vode
(cm nad morsko gladino)

Topo nivo
(cm nad morsko gladino)

Enak postopek, vendar zdaj v ArcView:

1. Zaženite ArcView, odprite nov projekt in nov pogled. Dodajte teme & # 145Pais_Pesca_country.shp & # 146, & # 145Flood_districts.shp & # 146, & # 145Flood_district_topo.shp & # 146 in & # 145Flood_levels.shp & # 146 iz mape & # 14512_Calc_with_grids. Projecirajte pogled (prek menijske vrstice: V iew / P roperties. / Projection., & # 145Equal area cilindrical & # 146) in nastavite enote razdalje na metre.

2. Najprej izdelajte topografski zemljevid poplavnih območij: Najprej želite izdelati mrežo teme & # 145Flood districts.shp & # 146 (T heme / Pretvori v G rid. Ime mreže: & # 145Mask & # 146, Output Mreža se razširi enako kot pogled, velikost celic 100 metrov), nastavite to mrežo kot masko v lastnostih analize in šele nato interpolirajte topolevele.

3. Vključite temo & # 145Flood_district_topo.shp & # 146, pojdite skozi menijsko vrstico do S urface / I nterpolate Grid. (razširitev izhodne mreže: enako kot pogled, velikost celice 100 metrov, metoda: IDW, polje z vrednostjo Z: kaj mislite? [16]). Ta interpolacija lahko traja dolgo. Shranite mrežo kot & # 145Floodtopo & # 146 v mapo, ki jo izberete. Po interpolaciji lahko naložite legendo, ki je bila narejena za to mrežo (& # 145Topo.avl ​​& # 146). Če se ne spomnite, kako naložiti legendo, si oglejte nasvet na strani 53.

SLIKA 14.2
Topografska raven vključenih okrožij

Če interpolacija traja predolgo, lahko postopek ustavite in naložite interpolirano mrežo. (V mapi & # 14512_Calc_with_grids & # 146, Vrsta vira podatkov: Vir podatkov mreže, & # 145floodtopo & # 146. Če mreže ne morete dodati v svoj pogled in ste prepričani, da je mreža v določeni podmapi, preverite, ali je pot do te podmape sledi načelu poimenovanja ArcView (glejte opombe k dogovoru o poimenovanju na strani 52, morda je nekje na poti do vaše datoteke presledek ali ime, ki je daljše od 13 znakov. Po dodal mrežo, naloži legendo [v urejevalnik legend]: & # 145topo.avl ​​& # 146). Rezultati vaših prizadevanj bi morali biti videti kot slika 14.2.

Prejšnja interpolacija je trajala tako dolgo, ker je moral ArcView izračunati raven vode za vsako celico v pogledu (3 767 celic s 3 983 celicami, kar je skupaj imelo 15 003 961 celic!). Čas izračuna lahko zmanjšate, ko zmanjšate število celic, ki jih je treba izračunati. To lahko storite s povečanjem velikosti celice. Če povečate velikost celic s 100 na 1 000, se število celic zmanjša na (377 * 398) = 150 046 celic. S tem bo mreža, ki jo ustvarite, imela precej nizko ločljivost, s katero je težko narediti smiselno analizo. Drug način za zmanjšanje števila izračunanih celic in ohranjanje enake ločljivosti (CellSize: 100) je povečati območje, na katerem želite, da se izračuna, in izhodno mrežo razširiti & # 145Same As Display & # 146. Če to storite, morate biti posebej pozorni, da vključite celotno območje, s katerim želite izračun, sicer je vaš izračun ničvreden in to morate storiti znova.

Zdaj morate v oktobru narediti mrežo z vodostaji. Oglejte si tabelo atributov teme & # 145Flood_levels.shp & # 146. Videli boste, da obstajata dva vodostaja na rekord, oktobrski in junijski vodostaj, za zdaj boste naredili mrežo z oktobrskim vodostajem.

4. Prepričajte se, da imate še vedno nastavljeno masko (v menijski vrstici: A nalysis / P roperties.) (To naj bo & # 145Mask & # 146, mreža, ki ste jo prej ustvarili na 3. od & # 145flood_district_topo.shp & # 146) . Aktivirajte temo & # 145Flood_levels.shp & # 146. V menijski vrstici pojdite na S urface / I nterpolate Grid. (Razširitev izhodne mreže: enako kot pogled [če je prejšnja interpolacija trajala dlje časa, lahko to čas zmanjšate tako, da povečate & # 145Flood_levels.shp & # 146 in izhodno mrežo razširite: enako kot prikaz], velikost celice 100 metrov , Metoda: IDW, polje Z-vrednosti: kaj mislite? [17]). Tudi to lahko traja dolgo in tudi za to interpolacijo je rezultat že v mapi, če komaj čakate, da se izvede interpolacija (& # 145Watersurf & # 146, legenda: & # 145Water_levels.avl & # 146).

Če ste mrežo interpolirali sami, jo lahko shranite kot & # 145Watersurf & # 146 (menijska vrstica: T heme / S ave Data Set.) V mapo, ki jo izberete, in spremenite ime te teme v Watersurf (Theme / Lastnosti.). Samo če spremenite ime teme (prek menijske vrstice: T heme / P roperties., Theme Name :), se bo to ime pojavilo v kazalu vsebine (na levi strani pogleda).

Zakaj ne vidite vodostaja v reki in velikega jezera v središču poplavnih okolišev? [18]

Zdaj imate v svojem pogledu dve mreži & # 145Watersurf & # 146 in & # 145Floodtopo & # 146 istega območja, obe z enakimi dimenzijami mreže 100 metrov. Globino vode v poplavnem območju izračunamo s formulo: Globina vode = gladina vode - raven Topo.

1. Pojdite na Alysis / M ap Calculator. prek menijske vrstice (slika 14.3). Odpre se okno Izračun zemljevida (slika 14.4).

SLIKA 14.3
Odpiranje menija za izračun zemljevida

SLIKA 14.4
Izračun globine vode v poplavnem območju

2. V oknu vidite imena mrež [Watersurf], [Floodtopo], [Mask] in [Mask.count]. Izračun, ki ga morate izvesti, bo opravljen s prvima dvema. Preprosto vnesite formulo tako, da dvokliknete [Watersurf], nato & # 145 - & # 146, nato pa dvokliknete [Floodtopo]. Za začetek izračuna kliknite Oceni in zaprite okno Izračun zemljevida, ko se rezultati prikažejo kot Tema & # 145Kračun zemljevida 1 & # 146. Shranite to temo [19] kot & # 145Fldepth & # 146, preden spremenite kar koli drugega. (T eme / Sa v e nabor podatkov.).

3. Ponovite legendo o & # 145Fldepth & # 146 tako, da voda postane različna preliva modre in suha zemlja en razred zelene (slika 14.5). Videli boste, da slednje ni lahko, saj morate razmišljati v negativnih vrednotah. Lažje je spremeniti izračun na Globina vode = Floodtopo - Watersurf. Ponovno opravite izračun, zdaj pa najprej [Floodtopo]. Pozitivne vrednosti pomenijo suho zemljo, negativne pa globina vode (slika 14.6). Shranite to temo kot & # 145fldepth2 & # 146.

SLIKA 14.5
Floodmap Pais Pesca

SLIKA 14.6
Floodmap Pais Pesca, obrnjen izračun

4. Projekt lahko shranite kot: & # 145Pais Pesca floodmap.apr & # 146 v mapo, ki jo izberete.

Med izračunom ali manipulacijo z mrežo lahko dobite naslednje sporočilo:

To pomeni, da ste iz delovnega imenika izbrisali mrežne datoteke. NIKOLI NE POČNI TO! Vedno uporabite Upravljanje virov podatkov v meniju Datoteka (prek F ile / M anage Data Source. V menijski vrstici, slika 14.7). Če to izberete, se odpre okno upravitelja virov (slika 14.8) in tukaj lahko kopirate, premikate ali brišete mreže.

SLIKA 14.7
Zagon upravitelja virov podatkov

SLIKA 14.8
Upravitelj virov

14.2. Prerazvrstitev

Izračun globine vode daje veliko število različnih globin vode v poplavnem območju Pais Pesca. Vendar nas pri nekaterih izračunih ne zanima globina vode, temveč le, ali je določeno območje poplavljeno ali ne. Delo z različnimi vodostaji naredi nadaljnje izračune po nepotrebnem okornim. Enostaven način za premagovanje te težave je prekvalifikacija podatkov, to pomeni, da daste določeno vrednost vsem slikovnim pikam, ki predstavljajo suho (0) območje, in drugo določeno vrednost (1) vsem slikovnim pikam, ki so poplavljene.

1. Nadaljujte tam, kjer ste se ustavili s prejšnjo vajo, ali odprite projekt, ki ste ga shranili: & # 145Pais Pesca Floodmap.apr & # 146.

2. Aktivirajte temo & # 145Fldepth & # 146.

3. Pojdite na A nalysis / R eclassify. prek menijske vrstice (slika 14.9) se odpre okno Prerazvrsti vrednosti. Za razvrstitev v suhe in poplavljene očitno potrebujemo le dva razreda (suha in poplavljena).

4. Kliknite Razvrsti. in izberite 2 razreda v oknu Classification (Slika 14.10) in kliknite OK.

5. Ponovno se prikaže okno Prerazvrsti vrednosti, zdaj z dvema razredoma. Vse negativne vrednosti, ki smo jih izračunali, so suha območja, zato uporabljate vrednosti -200 do 0 za suho in vrednosti 0 do 200 za poplavljena zemljišča. Kliknite V redu in prikazal se bo nov zemljevid (slika 14.11).

SLIKA 14.9
Odpiranje okna za ponovno razvrstitev

SLIKA 14.10
Izbira dveh razredov za prerazvrstitev

6. Datoteki dajte novo ime, uredite legendo (z dvojnim klikom na temo ali tako, da v menijski vrstici odprete T heme / Edit L egend.). Če ste pozabili, kako urediti legendo, si oglejte grafične prikaze v pogledu zemljevida, na strani 15.

7. Naredite si zemljevid poplave junija (slika 14.12) in projekt znova shranite.

SLIKA 14.11
Nova poplavna karta oktobra Pais Pesca

SLIKA 14.12
Floodmap junija

14.3. Poizvedovanje

V poizvedbi poskušate najti lokacije ali območja na zemljevidu, ki izpolnjujejo določena merila. To lahko storimo z dvema kriterijema z uporabo dveh GIS tem, lahko pa tudi bolj zapleteno poizvedbo z uporabo velikega števila tem.

Poiščite vsa območja s slanostjo 15 ppt in gojenje kozic kot glavne pridelke. Za to sta potrebni dve temi GIS, tj. Ribogojnice za slanost in kozice.

Poiščite vsa območja z aluvialnimi tlemi, srednjimi padavinami in rižem kot glavno kulturo. Tu so potrebne tri teme, tj. Karta tal, karta padavin in karta rabe zemljišč.

Poiščite vse vasi z več kot 50 odstotki ribičev, več kot 50 odstotki hindujskih hiš, povprečni dohodek teh gospodinjstev manj kot 150 USD / leto in ribolov v reki. Za to poizvedbo so potrebne štiri teme GIS, tj. Poklic, dohodek, religija in območje ulova.

14.3.1. Gojenje kozic na obalnih območjih Pais Pesca

V obalnih provincah Pais Pesca se je gojenje kozic hitro povečalo z 8 000 ha leta 1992 na 86 000 ha leta 2001. Uporabljeni sistem [20] je obsežen z nizko gostoto naselitve ličink, ki jih večinoma pridobivamo iz narave. Sistem je menjava riža s kozicami, riža z dežjem v mokri sezoni in enega pridelka kozic v sušnem obdobju. Povprečni donos kozic je približno 200-400 kg / ha / pridelek. Širjenje gojenja kozic pa ni bilo načrtovano in sčasoma so se razvili trije glavni problemi:

Sredi devetdesetih let so se pojavile prve večje težave z bakterijskim virusom Monodon (MBV), ki jim je leta 1998 sledil resen izbruh bolezni bele pege, ki je skoraj popolnoma izničil proizvodnjo kozic.

Nadalje se je zaradi širjenja in intenziviranja gojenja kozic razvil resen konflikt med velikimi rejci kozic in neoluščenimi rejci. To se je zgodilo, ko so kmetje kozic poskušali pridelati dva pridelka, posledično pa se je gojenje kozic podaljšalo v mokro sezono, kar je povzročilo vdor soli, ki je resnično oviral gojenje riža na istem območju.

Gojenje kozic je poseglo v gozd mangrov, glavni biosferni rezervat Pais Pesca. Ribniki s kozicami so bili zgrajeni v kislih sulfatnih tleh na območju mangrov, kar je povzročilo resno zakisanje površinske vode na začetku deževne sezone.

Leta 2000 je Ministrstvo za ribištvo izvedlo obsežno raziskavo na obalnem območju in zbralo naslednje informacije:

Lokacija in velikost ribogojnic s kozicami

Naredili boste analizo v GIS-u, da boste prikazali, kako se mreže in poizvedbe različnih mrež lahko uporabijo za podporo možnosti upravljanja gojenja kozic v Pais Pesca.

Ministrstvo za kmetijstvo je priporočilo zmanjšanje števila ribogojnic kozic v kmetijskih območjih obalnega pasu, to je na območjih z nizko slanostjo površinskih voda (5 ppt) v suhi sezoni, kjer je neoluščene rastline mogoče presaditi zgodaj v deževni sezoni. Zato je Ministrstvo za ribištvo zaprosilo za navedbo posledic te strategije.

1. Odprite ArcView, New Project, New View.

2. Nastavite delovni imenik na izbrani imenik (na primer: C: FAO_GIS Temp), projekcijo pogleda na & # 145Equal-Area Cylindrical & # 146 ter enote za razdaljo in zemljevid na metre.

3. V mapo Pogled dodajte naslednje teme iz mape & # 14513_Querying_shrimp & # 146: & # 145Pais Pesca country.shp & # 146, & # 145Shrimpfarms.shp & # 146, & # 145shrimp yields.shp & # 146 (vsi viri podatkov Feature ) in vir podatkov Grid & # 145Salgrid & # 146. Mapa vsebuje tudi legendo za mrežo slanosti (& # 145Salgrid & # 146) za uporabo, če želite.

Salgrid je ista mreža, kot ste jo naredili pri vaji na strani 58. & # 145Shrimpfarms.shp & # 146 je poligon v vseh ribogojnicah s kozicami na obalnem območju Pais Pesca. & # 145Shrimp yield.shp & # 146 točkovna datoteka lokacije vsake kmetije, njenih povprečnih donosov in drugih podatkov. & # 145Pais pesca country.shp & # 146 je poligon datoteka, ki vsebuje oris države.

4. Najprej želite vedeti, koliko ribnikov s kozicami se nahaja na območju z nizko slanostjo. To lahko storite s poizvedbo med dvema mrežama. Najprej morate torej pretvoriti & # 145Shrimpfarms.shp & # 146 v mrežo. Želite delati v hektarjih, zato izberete mrežno izhodno velikost CellSize 100 m (rezultat je 100 m x 100 m = 1 hektar). Za vrednosti celic uporabite polje & # 145Id & # 146 in ne pozabite preimenovati mreže v & # 145shrimps & # 146. Če želite, da vam ta izračun ne vzame preveč časa, boste morda želeli povečati farme kozic in narediti interpolacijo z obsegom izhodne mreže: enako kot prikaz.

To mrežo kozic morate poizvedovati z mrežo slanosti in jo uporabiti kot merila za izbiro: ribniki s kozicami = true, ([Shrimps] = 1.AsGrid) in salinity & lt = 5 ([Salgrid] & lt = 5). Z drugimi besedami: med vsemi mesti v temi o kozicah, kjer je prisotna kmetija (ali enaka želeni vrednosti slikovnih pik, v tem primeru 1, ker druge vrednosti ni), poiščite mesta, kjer je slanost (na salgrid Theme) je enako ali nižje od 5 ppt.

5. Pojdite na A nalysis / Map Q uery. prek menijske vrstice (slika 14.13).

SLIKA 14.13
Odpiranje poizvedbe po zemljevidu

6. Pojavi se okno Map Query in v okno morate postaviti izbirna merila: ([Kozice] = 1.AsGrid) in ([Salgrid] & lt = 5). To lahko storite na več načinov: Ta stavek lahko vnesete v spodnje okno ali pa izberete argumente v zgornjem delu okna Zemljevid poizvedbe Najprej dvokliknete [Škampi], nato kliknete & # 145 = & # 145, nato pa kliknete & # 1451 & # 146. Ko kliknete & # 1451 & # 146, se v vrstici poizvedbe prikaže: & # 1451.AsGrid & # 146. To je normalno, lahko pa uporabite tudi številko, ki ste jo vnesli vase (na primer & # 1451 & # 146), ki bo prav tako delovala. Nato kliknite & # 145in & # 146 sredi okna Map Query, nato dvokliknite [Salgrid], & # 145 & lt = & # 145 in vnesite & # 1455 & # 146. Iz dela vrednosti ne morete izbrati & # 1455 & # 146, zato morate to vrednost vnesti. (Slika 14.14). Kliknite Oceni.

SLIKA 14.14
Izbira meril za poizvedbo

SLIKA 14.15
Rezultati poizvedovanja gojišč kozic in slanosti

7. Poizvedba se bo zagnala in čez nekaj časa se bodo v pogledu prikazali rezultati poizvedbe, ki se imenuje & # 145Map Query 1 & # 146 (slika 14.15). Najprej zaprite okno Map Query 1 in nato ne pozabite shraniti te poizvedbe kot & # 145SHRSAL5 & # 146 [21] (T heme / Sa v e Dataset.). Če odprete tabelo atributov teme v mreži, boste videli, da je ribnik s kozicami približno 14 800 slikovnih pik, kar pomeni, da se v območju z nizko slanostjo nahaja 14 800 ha ribnikov s kozicami. Prvotno obliko datoteke smo pretvorili v mrežo z velikostjo celic 100 metrov na 100 metrov, kar je 1 hektar. Če se številka močno razlikuje od 14 800, morate preveriti projekcijo pogleda, da se prepričate, da je enako valjasta.

8. Da bi dobili jasnejšo sliko, želite vedeti tudi, koliko kmetij se nahaja na drugih območjih, in povprašamo po kmetijah s kozicami da in 5 & gt saliniteto & lt = 10. Z drugimi besedami, poiščite farme kozic, ki se nahajajo na območjih, kjer slanost je večja od 5 ppt in manjša ali enaka 10 ppt (slika 14.16).

SLIKA 14.16
Poizvedovanje z dvema mrežama in tremi kriteriji

9. Izpolnite tabelo 14.2:

TABELA 14.2
Slanost in število ribogojnic s kozicami

Saj vidite, da je več kot 50 odstotkov kmetij v območju slanosti 5-10 ppt. Toda še vedno približno 15 000 ha leži v območju z nizko slanostjo in vprašanje je, ali lahko dobite več informacij o stanju sistema kmetovanja na tem območju, preden se odločite, da jih zaprete. Za to morate pogledati donose kmetij. Lokacija in donos posameznih kmetij je na voljo v temi & # 145Shrimp yields.shp & # 146. To je oblika datoteke datoteke, tako da jo lahko uporabite za ustvarjanje mreže za donose.

10. Naredite površinsko mrežo donosov (s pomočjo interpolacije, menijska vrstica: S urface / I nterpolate Grid.), Velikost izhodne mreže 100 metrov, pri čemer je mreža kozic nastavljena kot maska. Ne pozabite, da to lahko traja nekaj časa! (Če želite skrajšati čas, povečajte temo & # 145Shrimp yield.shp & # 146 in določite obseg izhodne mreže: enako kot prikaz). Po končani interpolaciji lahko za to mrežo naložite legendo z imenom & # 145shrimp_yield_grid.avl & # 146 (v mapo 13_Querying_shrimp).

Če to storite pravilno, boste dobili mrežo, kot je predstavljena na sliki 14.17.

SLIKA 14.17
Donos kozic v različnih slanih conah Pais Pesca

Rezultati nekoliko spremenijo sliko. Na severovzhodu v območju slanosti 0-3 ppt vidimo pridelke (modre) približno 150-250 kg / ha / pridelek. V conah od 3 do 12 ppt so pridelki približno 250-450 kg / ha / pridelek (svetlo modra do rdeča), na jugu pa nenadoma opazite zelo nizke pridelke (temno modre) od 100-150 kg / ha / pridelek v slanosti od 12-15 ppt.

Prvi sklep bi lahko bil, da ministrstvu za kmetijstvo priporoči, naj zapre farme kozic v območjih slanosti 0-3 ppt. Ponovno poizvedite, koliko hektarjev kmetij za kozice bi bilo zaprtih po tej možnosti [22]. Drugič, v območju 12-15 ppt ste našli še en problem. Raven slanosti je ugodna za kulturo P. monodon, zato mora obstajati še en dejavnik, ki povzroča te zelo nizke donose v južnem delu.

V tematski tabeli v obliki obrazca & # 145Kmetije kozic & # 146 imamo podatke o boleznih kozic in kakovosti vode. S preprostimi poizvedbami v tej tematski tabeli lahko izberete kmetije z & # 145višjo boleznijo & # 146, pojavom & # 145polake bolezni & # 146, & # 145kakovostjo vode & # 146, & # 145razumno kakovostjo vode & # 146 itd. Poskusite to ven (ne pozabite: Najprej morate temo & # 145shrimpfarms.shp & # 146 pretvoriti v mrežo (menijska vrstica: T heme / Convert to G rid.), z velikostjo celice 100 metrov, pri tem pa izbrati ustrezno pretvorbeno polje , preden jo lahko poizvedujete v poizvedbi po zemljevidu).

Ugotovili boste, da je nizka proizvodnja na jugu povezana z & # 145pojavom visokih bolezni & # 146 in & # 145polako kakovostjo vode & # 146. Zdaj se postavlja vprašanje, kaj je vzrok te težave.

Dodajte temo & # 145Mangrove.shp & # 146. Vidite, da so kmetije z nizko proizvodnjo večinoma v pasu mangrov.

Koliko hektarjev farme kozic se nahaja v pasu mangrov?

1. Pretvorite temo & # 145Mangrove.shp & # 146 v mrežo z velikostjo mreže 100 metrov (preverite svoj delovni imenik in projekcijo, povečajte temo in naredite Obseg izhodne mreže pretvorbe: enako kot prikaz).

2. Poizvedite mrežo Mangrove z mrežo farme Shrimp (slika 14.18), ki vam bo dala sliko 14.19.

SLIKA 14.18
Poizvedovanje mreže mangrove z mrežo farme kozic

SLIKA 14.19
Farme kozic v mangrovem gozdu Pais Pesca

From the Theme table of the Map Query Theme you see that approximately 15 589 pixels are selected with this query, meaning that 15 589 ha of shrimp ponds are constructed in the mangrove belt of Pais Pesca (as one cell is 100 metres by 100 metres). Query further the Mangrove grid with the surface grid of the yields, after which you will get the following distribution (Table 14.3):

TABLE 14.3
Area of shrimp ponds according to average yields in the mangrove belt of Pais Pesca


Additional Useful Functions

There are a couple of other functions in bcdata that are useful to know when working with spatial data from the catalogue. bcdc_describe_feature gives the column names, whether the column is selectable, and the column types in both R and on the remote server:

This is a helpful initial step to learn column names and types when you construct your query.

Another useful function is show_query() which provides information on the request issued to the remote server:


Shared Flashcard Set

definition= "OR" boolean operator. creates new vector layer by placing two polygon layers on top of each other.

input=both layers must be polygon

output layer=always polygon

azimuthal-geometrically projected onto a plane. the point of the projection is at inifinity

used for prospective views of the whole planet

areas, shapes and directions only true at center point

projected onto a cone tangent at two standard parrallels

used to show aregion that is primarily east to west.

Universal transverse mercator

projected on to a cylinder with a 6° width

good for area with mostly a north to south extent

projected on a cylinder tangent to the chosen meridian

distances only true along the central meridian

distortions increase with distance from central meridian

a document or table that describes info about the data set

Position dilution of precision

-angles of the satelites (satelites require a 60º spread between them)

-obscured satelites(deep canyons, cliffs, tall buildings)

-recalibration of satelites

-multibeam (issues due to ionosphere disruptions)

uncorrected(single antennae, hand held GPS)

distance are only true along the equator

distortion increses with distance from equator

perimiter and ares of polygons as well as adjacency of polygons

used for spatial analysis by linking with attributes and locations

handeled differently in raster and vector

not a perfect correlation for measuring randomness

used for calculating distribution patterns

features on a continuous surface

combining lots of data layers

sopphisticated spatial modelling

where either of two polygons exist but not both

spatial relationships in a network (ie roads)

lots of attribute for each area

large spatial data queries

high quality detailed maps

based on the average distance between adjacent points

where either question occurs

data has only two possible values

use DEM (digital elevation model)

rise=the max difference between the cells z value and the z values if the 8 closest neighboring cells

geodetic-aligns spheroid to fit particular area

Geocentric-uses earths ceter of mass as the origin

not uniform units of distance

cant get accurate distances areas or directions between points

defined for locations with a 3-D sphere

referenced in lattitude and longitude

-points have zero dimensions so they cant realistically be portrayed in raster

-lines are 1 dimensional so they cant be accurately shown either

-small features can be lost or be hard to depict

-not usefulf for measuring perimiter

mathmatical model that describes the shape of the earth

different datums for different parts of the earth

geographic coordinate systems, locating data on a spherical surface(lats and longs)

projected coordinate systems, locating data on on to a flat surface (measured in meters)

-no spatial data only used to dsiplay info

only include features that the map maker decided to include

typically only shows one point in time

might only cover a limited area

a picture of all or part of the earth

shows where things are locted in relation to eachother

only shows part of what exists on earth at any one time

sends out a signal and times the response then uses this to create an image

uses ambient energy to create an image.

electromagnetic radiation is the most common form of energy used for this method

uses eye safe lazer of light to creat an image in a terestrial environment

-Lazer scanning and cooling system

-inertial navigation system

creates massive data sets

high speed autonmous post processing

-cancel out reduntant information

-create a new data layer that highlights certain features

-exagerate differences in a new layer

TOPOLOGY STORED =Perimeter and area of p olygons. Adjacency of polygons

for every polygon in GIS there will be information stored about which lines make it up and which polygons are next to each other (called contiguity)

affected by depth shorter wave lengths (blue) penetrat deeper than longer wave lengths (Red)

Turbidity- turbid water will scatter incident light and result in false bottom images


Efficient Processing of Spatial Group Keyword Queries

Efficient Processing of Spatial Group Keyword Queries XIN CAO, Queen's University Belfast GAO CONG and TAO GUO, Nanyang Technological University CHRISTIAN S. JENSEN, Aalborg University BENG CHIN OOI, National University of Singapore With the proliferation of geo-positioning and geo-tagging techniques, spatio-textual objects that possess both a geographical location and a textual description are gaining in prevalence, and spatial keyword queries that exploit both location and textual description are gaining in prominence. However, the queries studied so far generally focus on finding individual objects that each satisfy a query rather than finding groups of objects where the objects in a group together satisfy a query. We define the problem of retrieving a group of spatio-textual objects such that the group's keywords cover the query's keywords and such that the objects are nearest to the query location and have the smallest interobject distances. Specifically, we study three instantiations of this problem, all of which are NP-hard. We devise exact solutions as well as approximate solutions with provable approximation bounds to the problems. In addition, we solve the problems of retrieving top-k groups of three instantiations, and study a weighted version of the problem that incorporates object weights. We present empirical

Journal

ACM Transactions on Database Systems (TODS) &ndash Association for Computing Machinery


Geotracking

“Do you know where your assets are?” Chris Stern, director at Trimble Water, asks water utilities. Infrastructure is constantly being built over, and often there are only hand-drawn sketches available to pinpoint its location. Even in cases where good records are kept, such as Los Angeles, they’re still not digital. “It’s old technology that is inefficient.”

Asset management is a large challenge for customers, due to aging infrastructure and lack of investment. The historical approach held that pipes last 100 years and utilities should adopt a 1% annual replacement rate. Kelly Ball, senior consultant for PSD Software, estimates that cities typically rehab 20% of their manholes a year. But tracking the condition of assets can extend their life because it’s possible to “prolong the life of the system by avoiding events.”

Utilities need an asset registry to manage information about their assets, says Christa Campbell, industry specialist for global water practice at Esri, which she describes as the world leader in mapping and analytics. A geodatabase contains the location and all the information for each asset, including the installation date and type of material. “It should be an authoritative data source that maps assets, contains all relevant information, and helps to identify patterns.”

To remain accurate, a digital record should be updated as changes are made. “You have to know the condition of the assets so you can determine the highest-risk asset,” explains Stern. Therefore, he advises, it’s critical to capture good records.

GIS—a geographic information system for capturing, storing, managing, and analyzing data—can be used as a system of record and a system of engagement, making it easy to share information Campbell says. “Data collection in the field can validate and grow your asset registry, analysis supports activities such as leak detection, and web maps help to share information across your organization and with stakeholders. Management tools allow you to share with the right people, applications help with daily workflows like inspections, while story maps bring your work to life whether it’s about conservation or construction projects.”

Credit: TRIMBLE
Wireless pressure and infrastructure monitoring

TRIMBLE TRACKING
GPS has been used in a variety of industries, but is now providing broader solutions as it supports the life cycle of infrastructure such as pipes, valves, hydrants, and meters—and in wastewater, manholes, pumps, and lift stations.

Trimble’s smart water management software (Trimble Unity 3.8) is a cloud-based, GIS-centric software-as-a-service solution that runs on a mobile app and is suitable for the water, wastewater, stormwater, and environmental water industries. It works with an iPhone and is capable of 1-centimeter accuracy horizontally and vertically.

Furthermore, it enables customers to monitor operations in real time, assess the condition of assets, reduce leakage and non-revenue water, locate and map infrastructure, and track maintenance and repairs. Real-time data helps utilities make better decisions to predict and prevent failures.

Version 3.8 extends the platform’s capabilities to include proactive asset performance monitoring with the integration of Trimble Telog wireless Internet of Things remote monitoring sensors to measure and monitor water, wastewater, and groundwater systems for water pressures, flows, levels, and rainfall volumes. The software provides “layers of value,” says Stern, such as mapping and the condition of the asset, tracking for maintenance and repair, and real-time monitoring. The ability to provide an indication of performance data and the expected condition of the assets is hugely beneficial, allowing utilities to address the challenges associated with aging water infrastructure, leakage, and non-revenue water loss.

Credit: TRIMBLE
The latest technology from Trimble for asset management and mapping uses Mixed Reality technology. This shows high-accuracy Trimble mapping technology displaying buried infrastructure locations below the street based on GIS data.

Information about flow and pressure is important to collect. Spikes in pressure can cause damage to the pipes. A drop in pressure indicates a leak, which can lead to a pipe burst. It’s the same for sewer lines, which have an added environmental issue when overflows occur.

Trimble recently partnered with Aquarius Spectrum to distribute wireless leak detection and monitoring. Sensors on hydrants and valves assist an acoustic leak detection correlator in looking for lost revenue water. Trimble Leak Manager takes you to the area, while Trimble Leak Locator pinpoints the leak. “They work together,” summarizes Stern. Together, they are more precise and accurate, with fewer false positives—and contribute to as much as 18% reduction in water loss. “Utilities lose $12 billion in lost water revenue annually, so this is significant savings.”

Other benefits include reduced cost of repair and construction due to pinpointing the site—a big driver for utilities, Stern says, and improved asset performance, extended life, and reduced risk.

“If a pressure recorder finds a leak but doesn’t see water, and the utility doesn’t use a leak detector, the leak could burst the pipe,” cautions Stern. “That’s expensive. A UCLA campus flood caused by a sewer overflow cost $1 million in liability and fines.”

In addition, the value of water in drought areas is elevated. “They manage water as a valuable resource,” says Stern. Tracking non-revenue water is vitally important. “There’s no excuse not to use technology to be a steward of the environment.”

Unfortunately, too many have found an excuse. Stern says there are more than 3,000 using the technology, but many do so only temporarily for a study or project, rather than for permanent deployment. “They do small-scale studies to fix a problem. It’s a reactive approach: engineering problem-solving.” He calls it “lift and shift” as part of the capital improvement planning process to study the system. “For wastewater, it’s regulated that they must monitor overflows,” he explains, adding that EPA regulations have driven adoption of the latest technology in wastewater, while acceptance lags in water.

Barriers to adoption include lack of awareness adoption resistance by a utility, which Trimble tries to overcome by offering as a hosted service that provides alerts and alarms for the customer and funding. “It’s a challenge to update pipes at a static rate [by age],” notes Stern, “but you can reduce risk, improve performance, and prevent failure through monitoring.”

DATA SHARING
Esri’s analytics and mapping can supply data that provides an overall assessment of the system and its components. Do some areas have a record of leaks? Campbell queries. The same type of pipe? The same age? The same contractor? Corrosive soils?

Proactive planning is better than reacting to a crisis however, disaster preparedness and recovery can also be improved. A tracing tool identifies the asset’s location and then traces the system to determine which lines and valves to shut for maintenance or emergency repair. “It knows which valves are operational,” says Campbell. It can save time and money—drive time, search time with a map (which are often not updated), data entry, etc.

Vital information is shared with the office and the field to assist with disaster preparedness. That’s important because of a trend that sees roughly 60% of utilities staff reaching retirement eligibility within the next five years. “Most utilities function with in-house expertise, but they really need that information in the GIS system where everyone can access it,” explains Campbell. Domestically, GIS is here in a large part of the market, Campbell observes, but she is seeing increasing migration to web GIS, which field crews can access.

In addition to sharing information, mobile solutions allow crews to respond and prepare better on the East Coast, that might be hurricanes, Campbell says. Before the emergency, the system allows the authorities to manage road closures and sandbags. During the situation, it can alert to road closures and flooding, and after the crisis, it can collect damage data for funding and aid. “It helps with budgeting,” says Campbell, pointing out that utilities are often “strapped for cash.”

Credit: BENTLEY
A map of critical assets

CASE STUDY
Located on the Pacific Ring of Fire, the Philippines experience frequent earthquakes, volcanic eruptions, and typhoons that cause catastrophic losses. Manila Water Company Inc., in National Capital Region, Rizal Province, Philippines, prepared a Natural Calamity Risk Resiliency and Mitigation Masterplan to ensure that there is a reliable water supply in the event of a natural disaster for the service area covering the East Zone of Metro Manila (the National Capital Region) and Rizal Province.

Modeling with WaterGEMS illustrated what would happen if one or more interconnected supply systems shut down and which facilities would cause the most losses if they were operating at less than full capacity. The results helped Manila Water prioritize resiliency measures and contingency plans for more than 100 facilities to ensure that there is a reliable water supply during calamities. Estimates based on the simulations reduced the cost to restore reliable water service by $380 million, compared with $520 million without these measures.

Manila Water operates the concession to provide water treatment, water distribution, sewage, and sanitation services to the eastern side of Metropolitan Manila, where there are more than six million residential, commercial, and industrial customers. The concession encompasses 24 cities and municipalities in a 1,400-square-kilometer area. Manila Water has a mandate to provide customers with an uninterrupted water supply that complies with national drinking water standards. The masterplan saved $30 million in insurance costs through the end of Manila Water’s concession period.

Its goal is to mitigate the adverse effects of natural disasters on Manila Water customers and maintain reliable water service during natural disasters when it is essential for sanitation, hygiene, and preservation of life. However, the Philippines are threatened by an average of 20 typhoons every year, with 10 making landfall and five reaching superstorm proportions. In 2009, the deadliest season in decades, Typhoon Ketsana left more than 670 dead and caused $237 million in damages.

The country also suffers at least one destructive earthquake each year. When the magnitude 7.6 Samar earthquake struck in 2012, it displaced more than one million people and destroyed extensive infrastructure, leaving critical facilities inoperable, and disrupting water service. Government hazard assessments predict that the next catastrophic earthquake could cause as many as 34,000 fatalities and disrupt access to drinking water for months.

Credit: BENTLEY
WaterGEMS system analysis and design software

To assess preparedness for such a calamity, Manila Water conducted a Resiliency and Business Interruption study to determine which of its facilities would be the most vulnerable. The RBI study confirmed that the utility would suffer significant damage to dams, water transmission and distribution pipelines, treatment plants, reservoirs, pump stations, and other facilities. Initial damage assessments indicated that it would take $520 million to restore service after a calamity.

The utility concluded that it could not afford to lose these critical facilities and that it would take too long to restore them to full operational capacity. The RBI study suggested high-priority facilities that would need to be made more resilient to minimize damage. Lower priority facilities would require contingency plans in case of their loss. The objective was to mitigate the adverse effects of a natural disaster, ensure a reliable water supply during such calamities, and accomplish these objectives for the most economical cost. Savings would not only benefit the private utility and its public partners but also be passed on to customers in the form of lower tariffs.

WaterGEMS, Bentley’s water distribution analysis and design software, was used to build a model and simulate operations of the entire water supply system. The model incorporated data from internal and external sources, including ground elevations, demand loading and patterns, pipe profiles, and other parameters. Manila Water undertook a rigorous process to optimize the masterplan for improving resiliency and mitigating risk at its more than 100 facilities.

Simulating operations under various scenarios revealed the effects of losing one or more components of the water system, illustrating how interconnected systems would react if one or more systems shut down. The what-if scenarios included assessing options for evacuation sites near secure and reliable water supplies, network segmentation, water storage capacities, and other variables. The results allowed Manila Water to identify and prioritize critical facilities with confidence.

The modeling also helped the utility to make contingency plans in case of catastrophic losses. The simulations identified the best locations for underground emergency reservoirs to supply evacuation centers and other population centers if connecting systems were damaged. The masterplan also prioritized facilities whose failure would cause further damage, such as a dam that would cause a catastrophic release of water if it failed.

WaterGEMS produced project cost calculations, supporting documentation, and detailed reports for review. According to final RBI study projections, the WaterGEMS simulations demonstrated that the proposed measures would significantly reduce property damage and business interruption in Metro Manila and Rizal Province. More importantly, the plan would ensure a stable water supply for Manila Water’s customers.

“Mitigation of the adverse effects of a natural calamity is a race against time. Bentley’s WaterGEMS helped Manila Water minimize the amount of its investment while maximizing the resiliency and contingency of its facilities,” says Diogenes Adelbert Voltaire B. Evangelista, water system analysis and planning engineer, Manila Water Company.

Credit: ESRI
A web map displayed in a simple application that shares location data

PUTTING IT ALL TOGETHER
Stormwater monitoring using GIS is common in California for loss prevention and to track leaks. Quarterly certified inspections mandated by the EPA using GIS are conducted in a FOG environment to identify grease traps for violators. Sensors on manholes take readings of surges in flow—or lack of flow—to predict or see problems. A surge can predict a downstream overflow coming. “The software can predict and confirm an event, which can allow the utility to be pre-emptive, particularly for sanitary sewer overflow. They look through GIS at the area to see overflowed manholes, gather data, map to process, and log in to hot spots,” explains Ball.

Managing work operations around storm and surface water is only part of the broad-range solution offered by PSD Software’s HiperWeb. The software can also assist with compliance and permitting. Service lines can integrate with billing, inventory manifest, and distribution and collection lines. The software performs 2,300 processes invisibly. “The software tracks assets, personnel, and scheduling,” elaborates Ball. “It can integrate with data acquisition programs.”

Protocols are based on priority while routing optimizes workflow. “You want to react to an event in the most economic method possible,” notes Ball. “It becomes a function of operation and saves money.”

The life-cycle asset management application predicts the next water pump failure. “This is predictive and conditional through contract award and capitalization of replacement asset,” says Ball.

In addition, it can “normalize” data, Ball says, by working with all utilities to find commonality. “Contractors, crew—water or wastewater or public works—they’re all doing the same thing a different way.” This software provides a single platform for data, eliminating confusion.

Credit: ESRI
A water loss map

PAPERLESS
The trend goes beyond wastewater, says Adam Dinges, COO, Futura Systems Inc. “We eliminate the paper process for regulatory inspections and maintenance. Information is recorded in a native database, but is converted in GIS.”

Field workers are given an iPad that enables them to spatially see the GIS info and routes them to the exact location of the asset with directions for contractors, where they digitally record information, take a picture, and route an action—either to billing or to create a work order.

The importance of GIS tracking is that it stores information on the GIS server relevant to that feature: inspection dates, location, condition, etc. There’s no separate database. Information is shared by the feature everyone has the same data and information to be used for emergencies or maintenance. “It does away with contractor-specific forms,” says Dinges. It also provides more complete data in a standard form, which makes it easier to decipher. Previously, different data was collected by various sites or it was collected in different formats. “Data conversions are time-consuming and costly.” Utilities can choose what to share without exposing all the GIS data, he points out: no confidential information is shared.

The reason it’s important to have consistent data in an easy-to-understand format is to be able to see patterns in the data. Futura’s analytics tool, IQ, does this using historical data. “Maybe repairs are seasonal or in specific areas,” says Dinges. “This allows you to see trends and costs so you can budget.” It also allows you to see the number of repairs that have been performed. It makes the data usable, not just stored.

Dinges says there is a steady migration to GIS tracking. “Utilities are digitizing maintenance and inspection data at a rapid pace.” But he doesn’t see it stopping there. He anticipates widespread acceptance of the web map concept, in which data is posted in real time for utilities, police, medical facilities, and the community. “If a water main breaks, first responders could see the work done, the estimated time, the location, etc.”

That leads to savings of employees’ time and, ultimately, customer dollars. Dinges mentions one utility in Knox County, TN, that saved $150,000 a year in man-hours when they switched to digital backflow inspections. Rather than having to manually send the annual bill to customers for 40,000 water meter inspections, the system automatically sends information directly to the billing department. “It streamlined the process. There’s no storing manual records, no overlap of work.” Thus, fewer employees are needed—saving labor costs.

Customer service also benefits because as data is collected, it creates a history to build a database, which leads to predictive analysis and the ability to make changes accordingly. “You can see trends in the system,” says Dinges. “Who installed it, the type of parts used, which manufacturer’s part is failing…”

Dinges believes it signals a commitment from the utility to meet customer expectations when attention is paid to the quality of the equipment, equipment performance, and customer perception.

Previously, it was possible to see where an asset is, but now, it’s possible to know the asset’s make and other details, including recalls. When assets come from the manufacturer, information such as purchase date, manufacturer, and part number can be digitally input into the GIS database. “It’s the next level of GIS tracking.”

Analysis is the future, according to Campbell, who says that Insights for ArcGIS is the next step in analytics. “You can run it in an enterprise system or in the cloud.” Either way, it allows you to bring in spatial and non-spatial data, such as billing and business intelligence systems. “The strong analytical capabilities allow for on-the-fly comparisons. You can even share with other utilities for regional initiatives. GIS is limitless.”


Title: FASTMap v. 2010.01

FASTMap is mapping application available for the web or on mobile devices (IOS and Android) that browses geospatial data and produces detailed reports of objects within any area of analysis. FASTMap can access any geospatial dataset. The software can provide immediate access to the selected data through a fully symbolized interactive mapping interface. FASTMap can load arbitrary contours that represent a region of interest and can dynamically identify and geospatially select objects that reside within the region. The software can produce a report listing the objects and aggregations for the region, as well as producing publication quality maps. FASTMap also has the ability to post and maintain authored maps, any GIS data included in the map, areas of interest, as well as any titles, and labels. These defining ingredients of a map are called map contexts. These mao contexts can be instantly broadcast via the internet through any of an infinite number of named channels to small or large numbers of users monitouring any of the channels being posted to, so a user can author a map and immediately share that map with others instantly, whether they are on traditional desktop computer, laptop, mobile tablet or smartphone. Further, users receivingmore » broadcast maps can also alter the maps can also alter the maps, or create new ones and publish back to the channel in a collaborative manner. FASTMap can be configured to access virtually any geospatial data. « less


Speakers’ Biographies

Paul Thompson, Director Analytical Services – EMEA, Pitney Bowes Software
Paul has accumulated over 17 years of experience helping businesses harness the power of predictive analytics to grow their business. Working with some of the largest businesses in the world such as Home Depot, Pep Boys, IKEA, T-Mobile, and TJX, Dennys, Hooters, Rogers Communication, BBC, Japan Tobacco, Paul has developed and implemented state of the art real estate market optimisation systems and customer behavioural models that bridge the gaps between real estate and marketing. Paul has recently relocated from Toronto, Canada to head up the predictive analytics practice across EMEA.

Mark Thurstain-Goodwin is the founder and MD of Geofutures Ltd, an independent data, analysis and mapping software business, originally spun out of UCL in 2002. Having built Geofutures’ reputation delivering large-scale GIS projects such as the CLG Town Centres boundaries, property value models for Transport for London and Grosvenor Estates and the CPI spatial sampling model for ONS, Mark led the design of web-delivered mapping and data applications, enabling any organisation to access spatial data insight without software or training investment. The company’s latest innovation, The Knowledge Garden, takes the next step in meeting research and strategic needs by enabling teams to interact with maps, graphics and data and build insight together.

Mark was among the earliest MSc graduates in GIS from UCL’s Centre for Advanced Spatial Analysis, which he undertook following an early career in commercial property analysis. He also holds a Cambridge BA (Hons) in Geography, and remains an honorary fellow at CASA. Contact Mark at [email protected]

Nigel Dodd joined the Telefónica Dynamic Insights team earlier this year after nearly 25 years experience in food retailing with Tesco and Morrisons.

Following a degree in Philosophy, Politics and Economics, he joined the Tesco graduate training programme, performing a number of roles across Property, Marketing and Tesco.com including Director of Insight, Director of Tesco.com Marketing as well as heading up UK Site Research. Nigel joined Morrisons in 2007 as Insight Director, creating Morrisons first Insight department and supporting Marketing as Morrisons successfully made the transition from regional to national brand.

Crawford Davidson, MD in the UK at Telefónica Dynamic Insights , has extensive experience within the industry and has previously driven principal consumer data programmes as Marketing Director at Tesco.com, Tesco Direct and Clubcard in addition to Boots Advantage card.

Gary Powell is the UK Sales Director at Telefónica Dynamic Insights. With years of sales and marketing experience in data analytics companies including Acxiom, Experian, Equifax and Dun & Bradstreet, Gary heads up our sales team looking after our customers.

Ian Abbott, Business Development Manager, MapMechanics
Ian Abbott is a business development manager in the emerging markets team at MapMechanics. He previously worked for MapInfo and Pitney Bowes software for 14 years in a number of roles from support, consulting and presales. He has specialised in the retail and property sectors and has worked closely with many leading organisations ensuring that they have the best software and data for their needs and are able to use them to their full potential. He has a BSc in Geographical Information Systems from Kingston University.

About Blair Freebairn
o Founder and Principal Geolytix – 2011 present
o Global head of analytical data and routing at PB MapInfo – 2011
o Manager of the MapInfo UK predictive analytics (PA) business 2009-2011
o Principal architect and methodologist for EMEA PA 2005-2009
o Modeller working in UK retail 2001-2005
o Head of Site Research at UK largest leisure retailer 1997-2001
o Operational management for leisure retailer 1991-1997
o Over the last ten years have served the site research teams at the majority of the top 25 UK retailers

About GeoLytix
o Founded by Blair Freebairn in 2011 to deliver vendor neutral spatial analytical consulting, development and deployment services
o Sarah Hitchcock joined in Oct 2012 from managing the Network Planning team at J Sainsbury’s bringing additional business consulting, project management and training skills
o Recent projects have included bespoke store forecasting models, training, custom data development, system development and consulting projects
o Can be found via www.geolytix.co.uk or by email to [email protected]

Mark Stileman is a Propositions Manager at Ordnance Survey, responsible for developing new product and service concepts. Mark has played a leading role in developing new products enabled with the third dimension. He has a background in Geography, GIS and Marketing, and has previously worked for Halcrow and the Government of Bermuda.

Sarah Gibbons is the OS MasterMap Topography Layer Product Manager. Her background is predominantly in OS large-scales data Product Management including Product Manager for Land-Line (OS MasterMap Topography Layer’s predecessor) but also having spent some time within the OS Sales team as a Partner Account Manager.


Poglej si posnetek: Spatial Statistics Tools in ArcGIS