Ko'proq

Atributlar jadvalini interpolatsiyadan (grid) to'ldirmoqdamisiz?

Atributlar jadvalini interpolatsiyadan (grid) to'ldirmoqdamisiz?


Men yangi boshlang'ich ArcGIS Desktop 10 foydalanuvchisiman, xususiyati va pozitsiyasiga qarab panjara faylidan ma'lumotlarni formatlash formatiga qanday qo'shish kerakligi haqidagi savol bilan. Menda baliqning pozitsiyalari (x, y) va ularning harorati ko'rsatilgan formada rasm bor. Men ko'l yuzasidan tubigacha to'plangan harorat kuzatuvlaridan panjara yaratdim. Baliqlarning haroratiga qarab, interpolatsiyalangan yuzasidan baliq holatiga qadar chuqurlik qo'shishim kerak.

Shunday qilib, har bir baliq (aylana) uchun uning x o'qi bo'ylab joylashishini va uning haroratini ham bilaman.

Baliq harorati va uning x o'qi bo'ylab joylashishiga qarab panjaradan chuqurlikni qanday olish mumkin?


Tez va iflos usul bu XY nuqtalarini daryo og'zidan masofaga o'tkazish, undan daryo masofasi uchun foydalanish va keyin haroratni Y qiymati sifatida grafika qilishdir ... Agar qilayotgan ishingiz buning uchun to'g'ri chizilgan bo'lishga harakat qilsa grafik.

Agar siz 3D formatida biror narsa qilishga urinayotgan bo'lsangiz, z qiymatlarini ko'rib chiqing.

Agar sizga kerak bo'lgan narsa - bu shaklni shakllantirish uchun gridga qo'shilish bo'lsa, avval tarmoqni baliq tarmog'iga aylantiring.


# ushbu skriptni maxsus asboblar qutisiga qo'shing va uni ArcMap v 10.1 # # ---------------------------------- dan ishga tushiring. ----------------------------------------- import arcpy, os, traceback, sys try : def showPyMessage (): arcpy.AddMessage (str (time.ctime ()) + "-" + message) mxd = arcpy.mapping.MapDocument ("CURRENT") qatlamlari = arcpy.mapping.ListLayers (mxd) ## ASSUME MUNDARIJADA 1-QATMOQ UChUN QATMOQ - FISH fishLayer = qatlamlar [0] ## 2-QATUR TEMPERATURA RASTER BULADI. RASTER INFO-ni oling tempRaster = qatlamlar [1] g = arcpy.Raster (tempRaster.name) cSize = g.meanCellHeight yMax = g.extent.YMax; yMin = g.extent.YMin; nRows = g.height ## "FISH_Degr '" DAVOLIDA SAKLANGAN TEMPERATURANI TOPISHNI O'YLAYDI ## "CHUQURLIK" DAVOLASIDA CHIQARILGAN ChUQARIShNI JAVOB BERISh. HAM NUMERIC p = arcpy.Point () bilan arcpy.da.UpdateCursor (fishLayer, ("SHAPE @", "Fish_Degr", "DEPTH")) qatorlar qatorida: qatorlar qatori uchun: x = qator [0] .firstPoint. X fishTemp = qator [1] yCur = yMax pX = x; pY = yMin ## ENG MATCH MATCH TOPISH UChUN RASTER VA ITERATADAN UChUN ustunni oling myAr = arcpy.RasterToNumPyArray (g, p, 1, nRows) difMin = 100 oralig'ida (nRows): cellValue = myAr [nr, 0] difCur = abs (cellValue-fishTemp) agar difCur

shuning uchun bundan

buni olishingiz mumkin (agar siz haqiqatan ham buni xohlasangiz):


GIS-ga asoslangan ko'p maqsadli zarrachalar to'dasi Elektr transport vositalarining zaryad stantsiyalarini optimallashtirish

Ko'p ob'ektiv zarrachalar to'dasini optimallashtirish va GIS - bu quvvat olish stantsiyalarini rejalashtirishning amaliy usullari.

Jami xarajatlar va qoplanish o'rtasidagi Pareto egri chizig'i iqtisodiyot miqyosidagi ta'sir o'zgarishini ko'rsatadi.

EVlarning rivojlanishi va stantsiyalar konstruktsiyalari o'zaro bog'liqdir.

Yaqin kelajakda yanada yaxshi xizmat ko'rsatish qobiliyatiga ega zaryad stantsiyalari zarur.


Raqamli chiziqli grafika (DLG)

Xaritalarni formatlash sxemalari

Raqamli chiziqli grafik (DLG) faylidagi har bir xarita xususiyati mavjud atribut kodlari xususiyatning xususiyatlarini aniqlaydigan. Masalan, chiziq xususiyati asosiy yo'l va qurilish bosqichidagi atribut kodlariga ega bo'lishi mumkin. Atribut kodi ikkita butun sondan iborat: 3 xonali asosiy kodva 3 yoki 4 xonali kichik kod. Xususiyat kodlari standartlashtirilgan va hujjatlashtirilgan: Texnik ma'lumotlarga qarang.

A xaritani formatlash sxemasi atribut kodlarini displey uslublari bilan bog'laydigan qoidalarni o'z ichiga olgan fayl. Masalan, 170 212 kodli (to'rt g'ildirakli yo'llar) chiziqli xususiyatlar ingichka kulrang chiziq bilan chizilgan bo'lishi kerak.

Xaritalarni formatlash sxemalari kengaytmali fayllarda saqlanadi .mapformattingscheme. NMPlot yordamida xaritani formatlash sxemalarini yaratishingiz, ko'rishingiz va tahrirlashingiz mumkin.

Mavjud sxemani ochish uchun tanlang Ochiq NMPlot-dan Fayl menyusini tanlang, so'ngra xaritani formatlash sxemasi faylini tanlang. Xaritani formatlash sxemasining hujjat oynasi paydo bo'ladi.

Xaritalarni formatlash sxemasi qoidalari nuqta, chiziq yoki hudud xaritasi xususiyatlariga mos kelishiga qarab uch guruhga bo'linadi. Guruh qoidalarini tahrirlash uchun tugmachalardan birini bosing. Masalan, Agar chiziq qoidalarini tahrirlash tugmachasini bosgan bo'lsangiz, chiziq xususiyatlari uchun ishlatiladigan qoidalarni tahrirlashga imkon beradigan dialog oynasi paydo bo'ladi.

Xaritani formatlash sxemasidagi qoidalar panjara geografik izohlari uchun ishlatiladigan namoyish qoidalariga juda o'xshash. Iltimos, namoyish etish qoidalarini tavsiflovchi va ularni qanday tahrir qilishini muhokama qiladigan "Ko'rsatilgan geografik izohlarni" ko'rib chiqing. Xaritalarni formatlash sxemasi qoidalari uchta istisnodan tashqari bir xil tahrirda.

Xaritalarni formatlash sxemasi qoidalari jadvallarida qoida nomi bilan belgilangan qo'shimcha ustun mavjud. Ushbu ustun qoidaga tavsiflovchi nom berish uchun ishlatiladi.

Kategoriya va ism yorliqli ustunlar o'rniga xaritalarni formatlash sxemasi qoidalari jadvallari DLG atribut kodlari bilan belgilangan ustunga ega. Xaritani formatlash sxemasi qoidalari Grid Geographic Izohlarini ko'rsatish qoidalari izohlash toifasi va nomiga mos keladigan tarzda DLG xususiyati va # 39s atribut kodlariga mos keladi. DLG atribut kodlari ustuniga har bir qoida bilan bog'liq atribut kodlarini kiriting. Mana ba'zi misollar.

& # 34170 201 & # 34 DLG xususiyatlariga 170 201 atribut kodi bilan mos keladi.

& # 34170 201..205 & # 34 asosiy kodi 170 bo'lgan DLG xususiyatlariga va 201 dan 205 gacha kichik kodlariga mos keladi.

& # 34170 201,203 & # 34 asosiy kodi 170, yoki 201 yoki 203 kodlari bo'lgan DLG xususiyatlariga mos keladi.

& # 34170 201 va 170 603 & # 34 ikkala atribut kodlari 170 201 va 170 603 bo'lgan DLG xususiyatlariga mos keladi.

& # 34170 201, keyin 170 603 va # 34 DLG xususiyatlariga 170 201 va 170 603 atribut kodlari bilan mos keladi, 170 170 kodlari ro'yxatidagi 170 201 dan keyin 170 201 bilan.

& # 34 & # 34 atribut kodlari bo'lmagan DLG xususiyatlariga mos keladi.

Masalan, & # 34170 201-208 va 170 603,604 & # 34 barcha qurilayotgan asosiy va ikkilamchi yo'llarga mos keladi.

Xaritalarni formatlash sxemasi qoida jadvallarida Scale (1: x) deb nomlangan qo'shimcha ustun mavjud. Ushbu ustun mos keladigan DLG funktsiyalari ko'rsatiladigan minimal o'lchovni belgilashga imkon beradi. Agar uchastka qoida miqyosidagi quyida ko'rsatilgan bo'lsa, ushbu qoidaga mos keladigan har qanday DLG xususiyatlari ko'rsatilmaydi. Bu sizga kattalashtirish paytida batafsilroq ma'lumot beradigan xaritalarni tuzishga imkon beradi.

Eslatma:

NMPlot xaritani standart formatlash sxemasi bilan tarqatiladi. Ushbu sxema faylda Default.MapFormattingScheme, NMPlot o'rnatilgan katalogdan topish mumkin.

Eslatma:

Ushbu bo'lim atribut kodlari uchun faqat eng qisqa ma'lumotni taqdim etadi. Agar siz xaritani formatlash sxemalarini o'zgartirmoqchi bo'lsangiz, atribut kodlarini batafsil tavsiflovchi rasmiy DLG hujjatlarini oling. Hujjatlarni olish to'g'risida ma'lumot olish uchun texnik tafsilotlarni ko'ring.

Raqamli chiziqli grafika (DLG) parametrlari uchun dialog oynasi

DLG xaritasi fon qatlamini sozlash uchun Raqamli chiziqli grafik xaritasi qatlami parametrlari dialog oynasidan foydalaning.

Ushbu xarita qatlamining tavsiflovchi nomi: Ushbu xarita qatlamining qisqacha tavsifini kiriting. Misollar & # 34Yo'llar & # 34 va & # 34Suv xususiyatlari & # 34. Bu kelajakda ushbu qatlamni aniqlashga imkon beradi.

Ushbu xarita qatlamini izohlash uchun foydalaniladigan xaritani formatlash sxemasi: Ushbu DLG qatlamini ko'rsatish uchun ishlatiladigan xaritani formatlash sxemasini o'z ichiga olgan fayl nomini kiriting. Faylni ko'rib chiqishga imkon beradigan "Faylni ochish" dialog oynasini ko'rsatish uchun matn maydonining o'ng tomonida joylashgan "Browse" tugmachasini bosing. Xaritani formatlash sxemalari odatda kengaytmaga ega .mapformattingscheme. Xaritani formatlash sxemalarini ko'ring.

Ushbu xarita qatlamida koordinatalarni saqlash uchun foydalaniladigan geografik ma'lumotlar bazasi DLG fayli (lar): DLG fayllarini tanlang va # 39 geografik ma'lumotlar bazasini. Ma'lumotlar bazalarini tanlash bo'yicha ma'lumotni Datum Control-ga qarang. Ma'lumotlar bazalari haqida umumiy ma'lumot uchun ma'lumotlar bazalariga kirish bo'limini ko'ring.

Muhim:

Amerika Qo'shma Shtatlarining Geologik xizmati (USGS) tomonidan taqdim etilgan ko'plab DLG fayllari 1927 yildagi Shimoliy Amerika ma'lumotlar bazasida (NAD-27) mavjud. DLG fayllaringiz uchun to'g'ri ma'lumotni bilganingizga ishonch hosil qiling.

Ballarni, chiziqlarni, yo'nalishlarni ko'rsating: Ko'rsatmoqchi bo'lgan geografik xususiyatlarga mos keladigan katakchalarni belgilang. Odatda, uchta quti ham tekshirilishi kerak. Ammo, agar kerak bo'lsa, siz qaysi xususiyatlar ko'rsatilishini cheklashingiz mumkin. Masalan, faqat nuqta geografik xususiyatlarini namoyish qilishni tanlashingiz mumkin.

Ushbu xarita qatlamini o'z ichiga olgan DLG fayl (lar) ning nomlari: Ushbu qatlam tarkibida ko'rsatmoqchi bo'lgan DLG fayllarining nomlarini kiriting. Har qanday sonli fayllar ro'yxatiga kiritilishi mumkin. Fayl nomini ko'rish maydonining o'ng tomonida joylashgan Browse tugmalaridan birini bosib, faylni ko'rib chiqishga imkon beradigan Open File dialog oynasini oching. DLG fayllari odatda kengaytmaga ega .dlg.

Fayl qo'shish uchun ro'yxatga fayllarni qo'shish tugmasini bosing. Sichqoncha bilan faylni bosish orqali faylni olib tashlang va so'ngra Fayllarni ro'yxatdan olib tashlash tugmachasini bosing.

Texnik ma'lumotlar

NMPlot 1: 24000 va 1: 100000, 3-darajali, ixtiyoriy formatdagi DLG fayllarini o'qiy oladi.

DLG faylining koordinata tizimi Universal Transverse Mercator (UTM) bo'lishi kerak.

DLG fayli DLG aniqligi yozuvlarini o'z ichiga olmaydi.

NMPlot quyidagi turdagi DLG yozuvlarini namoyish qilishi mumkin.

Yozuv turlarining aralashmasini o'z ichiga olgan DLG fayllari qo'llab-quvvatlanadi.

NMPlot bir nechta DLG fayllarini o'z ichiga olgan qatlamlarda DLG topologiyasi ma'lumotlarini birlashtirishi mumkin.

DLG formati Amerika Qo'shma Shtatlari Geologiya xizmati (USGS) Milliy xaritalash bo'limi tomonidan boshqariladi. Rasmiy spetsifikatsiyalar USGS veb-saytida joylashgan http://www.usgs.gov & # 34 raqamli chiziqli grafikalar uchun standartlar & # 34 nomli texnik hisobotda hujjatlashtirilgan. 2000 yil sentyabr oyidan boshlab ushbu hujjatning URL manzili http://rockyweb.cr.usgs.gov/nmpstds/dlgstds.html edi. Ushbu hujjat barcha DLG atribut kodlari ro'yxatini o'z ichiga oladi.


Atributlar jadvalini interpolatsiyadan (grid) to'ldirmoqdamisiz? - Geografik axborot tizimlari

Shahar ko'llari shahar uchun juda qimmatbaho chuchuk suv manbalari hisoblanadi. Ular nafaqat yashash va ishlab chiqarish uchun suv etkazib berishadi, balki shahar atrofini tartibga solish, toshqinlarni saqlash, qishloq xo'jalik maydonlarini sug'orish va shahar landshaftini obodonlashtirishda ham muhim rol o'ynaydilar [1 & # x2013 5]. Shunday qilib, ular shahar rivojlanishi uchun juda qadrlidir. Biroq, so'nggi yillarda urbanizatsiya tufayli shahar ko'llari katta xavf ostida qoldi [6 & # x2013 8]. Ko'llar maydoni kamaytirildi [9, 10] suvning sifati ifloslandi [11, 12]. Ko'llardagi bu zarar ba'zi hollarda qaytarib bo'lmaydigan darajada. Masalan, Xitoyning Uxan shahrida ko'chmas mulk rivoji tufayli ko'plab ko'llar xaritadan butunlay g'oyib bo'ldi [13]. Shuning uchun shahar ko'llarining shahar rivojlanishiga qarab o'zgarishini o'rganish juda muhimdir.

Ko'l hududining o'zgarishi odatda suv resurslarining miqdoriy o'zgarishi sifatida qabul qilinadi. An'anaviy tadqiqotlarda suv muvozanati tenglamalari asosida bir qator gidrologik modellar ishlab chiqilgan. Boshqacha qilib aytganda, tizimdagi suvning o'zgarishi tizimning chiqishi va kirishi o'rtasidagi farqga teng keladi [14, 15]. So'nggi yillarda ba'zi tadqiqotlar shaharlashuv va inson faoliyatining shahar ko'llariga ta'siriga qaratilgan. Ko'llarga etkazilgan zararni suv balansi tenglamasi bilan baholash mumkin edi [16, 17]. Biroq, an'anaviy gidrologik usul uchun tarixiy ma'lumotlarning ko'pligi kerak, odatda to'plash qiyin. Boshqa tomondan, yuqori aniqlikka erishish uchun gidrologik modellar juda murakkablashadi.

Bundan tashqari, ko'l hududining masofadan turib zondlash (RS) yordamida o'zgarishini kuzatish keng qo'llanilgan [18, 19]. Suvning spektral imzosiga ko'ra ko'l hududini tasvirni qayta ishlash usullari yordamida masofadan turib zondlash tasvirlaridan tezda aniqlash mumkin [20]. Turli davrlarda ko'l hududining o'zgarishini geografik axborot tizimlarida (GIS) fazoviy tahlil qilish yo'li bilan olish mumkin [21, 22].

Oldingi yutuqlarga qaramay, ko'llar hududining o'zgarishi to'g'risida hali ham ma'lumot yo'q. Birinchidan, avvalgi tadqiqotlardagi o'rganish maydoni odatda barcha shahar ko'llari o'rniga ma'lum bir ko'l suv havzasi sifatida qabul qilingan. Ikkinchidan, ko'llar hududining RS tomonidan o'zgarishini kuzatib borish statik usul hisoblanadi. U ma'lum vaqt ichida ko'l hududining o'zgarishi jarayonini ko'rsatolmaydi.

Ushbu maqola shaharning barcha ko'llariga qaratilgan. Ushbu ko'llar hududining o'zgarishi naqshini topish uchun ushbu maqolada ma'lumot olish uchun shahar evolyutsiyasi usuli ishlatilgan. Xususan, Xitoyning Uxan shahrida o'tkazilgan amaliy tadqiqotda shahar ko'llarining o'zgarishini modellashtirish uchun uyali avtomatlar va ko'p modentli tizimlar joriy etildi.

Uyali avtomat (CA) - bu diskret vaqt, makon va holatga ega bo'lgan dinamik model. U qo'shni hujayralar orasidagi oddiy aloqalar orqali murakkab tizimning fazoviy-vaqtli evolyutsiyasini simulyatsiya qilishga qodir [23]. Uyali avtomatlar matematika, fizika, biologiya va murakkablik fanlarida, ayniqsa shahar o'sishi va erdan foydalanish va er qoplamining o'zgarishi (LUCC) tadqiqotlarida keng qo'llanilgan [24 & # x2013 27]. Shahar ko'llari geografik jihatdan shahar makonining bir qismidir. Shunday qilib, shahar ko'llarining o'zgarishini LUCCning bir qismi deb hisoblash mumkin. Boshqacha qilib aytganda, shahar ko'llarini murakkab tizim sifatida tan olingan shahar tizimining quyi tizimi deb hisoblash mumkin. Shu sababli, shahar ko'llarining hududiy evolyutsiyasini uyali avtomatlar bilan taqlid qilish mumkin.

Biroq, CA modeli shahar rivojlanishi va inson faoliyatining shahar ko'llariga ta'sirini alohida-alohida to'liq aks ettira olmaydi, chunki qo'shnichilik munosabatlari faqat shahar ko'llari va LUCC o'rtasidagi o'zaro ta'sirlarni ko'l bo'yida aks ettirishi mumkin. Shahar ko'llarida odamlarning turli xil harakatlarini e'tiborsiz qoldirib bo'lmaydi. Masalan, hukumatlar ko'llarning ekologik muhitini va uning atrofidagi landshaftlarni muhofaza qilish uchun qonunlar va qoidalar chiqarishi mumkin. Aksincha, ko'chmas mulk ishlab chiqaruvchilari qurish va rivojlantirish uchun ko'proq erlarga muhtoj. Bu ko'llarni to'ldirishiga olib kelishi mumkin.

CA modelidagi cheklovlarni engib o'tish uchun odamlarning xulq-atvori va qaror qabul qilishini aks ettiruvchi multiagentli tizim (MAS) ishlatilgan. Agentlik tushunchasi sun'iy intellekt (AI) sohalaridan kiritilgan. Agent mustaqil ravishda qaror qabul qila oladigan shaxsni haqiqatda aks ettirishi mumkin [28, 29]. MAS modeli murakkab tizimlarni tahlil qilish va simulyatsiya qilish uchun ishlatilishi mumkin. Ayniqsa, shahar tizimlarida MAS modeli transport, aholi va iqtisodiyot kabi ko'plab sohalarda keng qo'llanilgan [30, 31]. Ko'pagentli tizimni uyali avtomatlar bilan birlashtirish hozirgi vaqtda shahar evolyutsiyasi va LUCC tadqiqotlarida tendentsiyaga aylandi [32 & # x2013 35].

Ushbu maqolada MAS-CA modeli asosida Shahar ko'lining dinamik rivojlanish evolyutsiyasi modeli (DULAEM) ishlab chiqilgan. DULAEM-da CA modeli qo'shni hujayralar o'rtasidagi munosabatlar orqali ko'l bo'yidagi atrof-muhit omillarini rivojlantirish uchun ishlatiladi. MAS modeli odamlarning xulq-atvori va qarorlarni qabul qilishda taqlid qilish uchun ishlatiladi. Ushbu maqolada shahar ko'llari bilan bog'liq agentliklar hukumat agentlari, ishlab chiquvchilar agentlari va rezident agentlarni o'z ichiga oladi.

CA modeli odatda panjara tuzilishiga asoslangan. Tarmoqli geografik axborot tizimi (GIS grid) fazoviy ma'lumotlar uchun juda samarali va shahar boshqaruvida keng qo'llanilgan [36 & # x2013 38]. Biroq, vektor va raster kabi tarmoqning an'anaviy ma'lumotlar tuzilmalari yuqori fazoviy rezolyutsiya va samarali hisoblash o'rtasidagi ziddiyatni bartaraf eta olmaydi. Rastr modeli juda sodda va tezkor, shuning uchun u juda ko'p miqdordagi panjara va fazoviy tahlilni boshqarishi mumkin. Xususan, raster modeli uyali avtomatlar va qo'shni hujayralarni qidirish uchun samarali. Shu bilan birga, diskret geografik xususiyatlar, masalan, shahar ko'llari uchun, raster modeli har bir ko'lning katak hujayralarini ajratib olish qiyin. Vektor modeli ko'l hududlarini hisoblashda raster modelga qaraganda qulayroq. Agar kataklarning kattaligi kichik bo'lsa va panjara soni massiv bo'lsa, bu juda samarasiz bo'ladi. Vektorning nuqsonlarini bartaraf etish uchun Deren Li ko'p darajali muntazam vektorli kataklardan tashkil topgan Fazoviy Axborot Multigrid (SIMG) kontseptsiyasini taklif qildi [39]. SIMG tarmoqni qidirish samaradorligini oshirishi mumkin. Biroq, shahar ko'llarining fazoviy shkalalari xilma-xil bo'lganligi sababli, har xil darajadagi panjara o'lchamlarini aniqlash qiyin. Shunga qaramay, SIMG diskret geografik xususiyatlarni turli fazoviy o'lchovlar bilan modellashtirish g'oyasini taklif qildi. SIMG asosida ushbu ishda Urban Lakes Multilevel Grid (ULMG) ishlab chiqilgan bo'lib, u muntazam tuzilmani tartibsiz tuzilishga birlashtirgan va vektor modelining ustunligini raster modeli bilan birlashtirgan [40].

2. Materiallar va usullar 2.1. O'qish maydoni va ma'lumotlar

Ushbu maqolada biz Uxan shahridagi shahar ko'llariga e'tibor qaratamiz. Vuxan - Xitoyning markaziy qismidagi yirik shaharlardan biri va Yangtsi daryosining o'rta qismida joylashgan. Shakl 1da ko'rsatilgandek 13 ta tuman mavjud. Vuxan shahrining umumiy maydoni 8494.14 & # x2009km 2 ni tashkil qiladi va hozirda aholisi o'n milliondan oshdi. 1990-yillardan boshlab, Xitoy iqtisodiyotining jadal rivojlanishi bilan Vuxan keskin kengayib bormoqda. Shunday qilib, shahar ko'llarining kengayishi shaharlarning tez kengayishi bilan jiddiy ravishda siqilgan edi. Shu sababli, biz Uxan shahridagi shahar ko'llarini 1991 va 2002 yillarda qazib oldik. Bu davrda Uxan hukumati hali er usti suv resurslari uchun kuchli nazorat mexanizmini o'rnatmagan edi, shunda ba'zi shahar ko'llari to'ldirildi.

Uxan shahrining joylashuvi va ma'muriy bo'linmalari.

1991 va 2002 yillarda Uxan shahridagi shahar ko'llari Landsat TM / ETM & # x2009 & # x2b & # x2009 rasmlaridan olingan. Ushbu tasvirlar 7 ta spektral tasmadan iborat. Har bir tasmaning fazoviy o'lchamlari 30 & # x2009m. Ko'llar Modified Normalized Difference Water Index (MNDWI) tomonidan chiqarilgan. MNDWI shahar tasvirlarida Normalize Difference Water Index (NDWI) ga qaraganda aniqroq, chunki MNDWI binolarning shovqinini jilovlay oladi [41].

1991 va 2002 yillarda Uxan ko'llari 2-rasmda ko'rsatilgan. 93 ta ko'l mavjud. Har bir ko'lning maydoni 1991 yilda 100000 dan ortiq. 2-rasmga binoan 1991 va 2002 yillar davomida 93 ko'lning maydoni 35,77 & # x0025 ga kamaygan va ularning o'ntasi butunlay yo'q bo'lib ketgan.

Landsat tasvirlaridan 1991 va 2002 yillarda olingan shahar ko'llari.

Bundan tashqari, LUCC ning ko'l bo'yidagi ko'llarga ta'sirini baholash uchun biz ko'llar atrofida 100 & # x2009m bufer yaratdik. Tampon zonalarida erdan foydalanish qazib olingan va 1-jadvalda 5 toifaga ajratilgan.

ULMG asoslari 3-rasmda keltirilgan. ULMG ikki darajali katakchalardan iborat. Birinchi darajali panjara tartibsiz va vektor modelini qabul qiladi. Ikkinchi darajali panjara kvadrat raster modeliga asoslangan. Ikkinchi darajadagi panjara Landsat tasvirlari piksellari bilan bir xil o'lchamda aniqlanadi.

Boshlang'ich katakning satr raqami va ustun raqami (yuqori chap burchakdan).

Qator va ustunlar raqamlari.

Joylashuv ma'lumotlaridan tashqari, har bir ko'lning ba'zi bir atribut ma'lumotlari, masalan, ko'l identifikatori (LID), ko'l nomi va ko'l zonasi, tegishli birinchi darajali tarmoqning atributlar jadvaliga yozilishi mumkin. Boshqa tomondan, ikkinchi darajadagi har bir tarmoq faqat 1-jadvalda belgilangan identifikator raqami bilan ushbu tarmoqning erdan foydalanish turiga mos keladigan piksel qiymati bilan ifodalanishi mumkin.

Agar shahar ko'llarining tarqalishi haddan tashqari zich bo'lsa, ularni chegaralovchi to'rtburchaklar bir-biriga to'g'ri kelishi mumkin. Bunday holda, har bir ikkinchi darajali katakchaning piksel qiymati uning LID-dan oldin qo'shiladi. Masalan, piksel qiymati 2634 bo'lsa, bu ikkinchi darajali tarmoq 263-sonli ko'lga tegishli ekanligini anglatadi va uning erdan foydalanish turi 4-toifadagi rivojlangan er hisoblanadi.

Landsat tasvirlaridan ULMG qanday yaratilishi 4-rasmda ko'rsatilgan. Ikkinchi darajali katakchani to'g'ridan-to'g'ri rasmlardan olish mumkin, shunda ikkinchi darajali katakning fazoviy o'lchamlari tasvirlarni & # x2019 da 30 & # x2009m da kuzatib boradi. Birinchi darajali panjarani yaratishdan oldin, tasvirlardan olingan ko'llarni vektorli modelga aylantirish kerak. Nihoyat, joylashuv ma'lumotlari va LID kabi ba'zi parametrlar atributlar jadvaliga yoki piksel qiymatiga qo'shilib, ushbu ikki darajali katakchalarni birlashtirishga imkon beradi.

Landsat rasmlaridan ULMG yarating.

5-rasmga binoan Uxanning ULMG hosil bo'ladi. Turli xil o'lchamdagi 93 ta to'rtburchaklar mavjud va ularning ba'zilari bir-biri bilan qoplangan. Ikkinchi darajali panjaraning balandligi 5529 piksel va kengligi 4789 piksel. Sharqiy ko'lda uning kelib chiqishi panjarasi (yuqori chap burchakdan) 2915 qatorda va 2224 ustunda joylashgan. Ikkinchi sathda 442 qator va 418 ustun mavjud. Binobarin, ULMG raster modeli asosida ikkinchi darajali panjara orqali tezkor kirishni amalga oshirishi mumkin, chunki agar vektor modeli samarasiz bo'lar edi, agar panjaralarning umumiy soni 10 5 dan oshsa. Shu bilan birga, vektor modeliga asoslangan birinchi darajali tarmoq moslashuvchanlikni oshirishi va keraksiz ma'lumotlarni kamaytirishi mumkin. ULMG uyali avtomatlar va multiagentli tizimlar asosida shahar ko'llarining o'zgarishini o'rganish uchun mustahkam asos yaratadi.

2.3. Shahar ko'lining dinamik evolyutsiyasi modeli

CA modelini MAS modeli bilan birlashtirish shaharlarni kengaytirish va LUCC tadqiqotlarida keng qo'llanilgan. Ushbu maqolada ma'lumot olish uchun avvalgi tadqiqotlar va usullardan foydalanilgan va CA modeli va MAS modeli asosida Dinamik Urban Lake Lake Evolution Model (DULAEM) ishlab chiqilgan.

6-rasmda ko'rsatilgandek, DULAEMda ikkita qatlam mavjud. Shahar ko'llari va ularning atrofi o'rtasidagi o'zaro ta'sirlarni modellashtirish uchun CA qatlami quyida joylashgan. CA qatlami asosida MAS qatlami hukumat, ko'chmas mulk ishlab chiqaruvchilari va aholining xulq-atvori va qarorlarini qabul qilishni modellashtirish uchun mo'ljallangan. CA qatlami va MAS qatlami ULMG bilan birlashtirilgan.

DULAEM tuzilishi. CA qatlami va MAS qatlami ULMG-ga birlashtirilgan.

CA qatlamida shahar ko'llari va ularning atrofi o'rtasidagi o'zaro ta'sir ko'l hujayralari va ularning atrofidagi hujayralar o'rtasidagi munosabatlarda aks etishi mumkin. Boshqacha qilib aytganda, boshqa ko'l hujayralari bilan o'ralganidan ko'ra, erdan foydalanish hujayralari bilan o'ralgan ko'l hujayrasi faolroq o'zgaradi. Ko'l hujayralarining bunday o'zgarishi odatda ko'llar bo'yida sodir bo'ladi. Shuning uchun DULAEM dagi CA qatlami an'anaviy CA modellaridan farq qiladi.

(1) Hujayra maydoni. Yuqorida ta'kidlab o'tilganidek, ULMG ning ikkinchi darajali panjarasi CA qatlamining kataklari deb hisoblanishi mumkin bo'lgan ko'plab kvadrat panjaralardan iborat. Shunday qilib, CA qatlamining hujayra maydoni to'g'ridan-to'g'ri ULMG dan olinadi. Boshqacha qilib aytganda, hujayra makonining darajasi ikkinchi darajali panjara bilan bir xil. Har bir katakchaning kattaligi 30 & # x2009m & # x2009 & # x2217 & # x200930 & # x2009m ni tashkil etadi, bu ikkinchi darajali katak va Landsat rasmlarining piksel o'lchamiga teng. Ko'l bo'yida erdan foydalanishni qazib olish uchun har bir ko'l atrofida 100 & # x2009m bufer mavjud. Shu sababli, har bir buferning cheklangan to'rtburchagi ULMG asoslariga ko'ra yangi birinchi darajali panjara sifatida ishlab chiqilgan. Hujayra makonining chegarasi yangi birinchi darajali panjaraning chegarasidir. Shunday qilib, faqat birinchi darajali panjara bilan qamrab olingan mintaqalar haqiqiy hujayra maydoni hisoblanadi.

(2) Uyali davlatlar. DULAEM ning CA qatlamida hujayralar ko'l hujayralariga (LC) va erdan foydalanish hujayralariga (LUC) bo'linadi. Ularning holatlari S tomonidan ramziy ma'noga ega Ko'l va S ernavbati bilan.

1-jadvalda ko'rsatilganidek, S er erdan foydalanish toifalarining har biriga mos keladigan beshta davlatni o'z ichiga oladi. Shunday qilib, S er (1) S land & # x3d s 1 & # x2264 s & # x2264 5, & # x2003 s & # x2208 Z sifatida erdan foydalanish turi identifikator raqami bilan ifodalanishi mumkin.

Uni erdan foydalanish hujayralaridan farqlash uchun S Ko'l (2) S ko'l & # x3d 10 & # x2b N sifatida ikki xonali raqam sifatida ifodalanadi, bu erda N - ko'l xujayrasining hissa qo'shadigan joylari soni.

(3) Mahalla. DULAEM ning CA qatlamida mahalla maydoni Mur mahallasi sifatida aniqlanadi, ya'ni sakkizta qo'shni hujayralar markaz hujayrasini o'rab oladi. Mur mahallasidagi hujayra erdan foydalanish hujayrasi yoki ko'l xujayrasi bo'lishi mumkinligi sababli va faqatgina mahalladagi erdan foydalanish xujayralari ko'l hujayralarining o'zgarishiga yordam beradi, chunki ko'l xujayrasi uchun uning erdan foydalanish xujayralari mahallasi ishlaydi. Agar ushbu qo'shni mahallalar soni N bilan ifodalangan bo'lsa, N ning qiymati quyidagicha ko'rsatiladi: (3) N & # x3d e 0 & # x2264 e & # x2264 8, & # x2003 e & # x2208 Z.

7-rasmda ba'zi hollarda N qiymatlari ko'rsatilgan. Masalan, ko'l xujayrasi uchta ko'l hujayrasi va uning Mur mahallasida beshta erdan foydalanish hujayralarining yonida joylashgan. Shunday qilib, namuna hujayralarining N qismi beshta. 7-rasmdagi har xil N ni taqqoslab, N o'zgaruvchisi ko'l xujayrasi inson faoliyati ta'sirini qanchalik ko'rsatishini ko'rsatishi mumkin. Xususan, N kattaroq bo'lsa, ko'l hujayrasi odamlarning ishiga qanchalik sezgir bo'ladi va hujayra holatining o'zgarishi ehtimoli shunchalik yuqori bo'ladi. Aksincha, N kichikroq bo'lsa, inson xujayralarining quruqlikdagi hujayraga ta'siri shunchalik kam bo'ladi, shuning uchun ko'l xujayrasi shunchalik barqaror bo'ladi. Agar ko'l xujayrasi boshqa ko'l hujayralari bilan o'ralgan bo'lsa, hujayra holati o'zgarmasdir. Shu sababli, shahar ko'llarining maydon o'zgarishi kamida bitta erdan foydalanish hujayralari mahallasiga ega bo'lgan ko'l hujayralarining holatidagi o'zgarishlarda aks etadi.

Ko'l hujayralari uchun hissa qo'shadigan mahallalar soni (N).

Hujayra holati, ya'ni 1-jadvaldagi erdan foydalanish turi, Faoliyatning boshlang'ich qiymatini aniqlaydi. Masalan, o'simliklarning ekotizimi nisbatan barqaror bo'lib, o'simlik bilan birga erdan foydalanish hujayrasi o'zgarishi mumkin emas. Qishloq xo'jaligi erlari va rivojlangan erlar har ikkala inson faoliyatining asosiy mintaqalaridir, shuning uchun ular ko'proq Faoliyatlarga ega [42]. Shunga ko'ra, erning boshlang'ich faoliyati hujayralardan foydalanadi (I er) 2-jadvalda keltirilgan.

Mahalla: agar erdan foydalanish xujayrasi boshqa erdan foydalanish turlaridan biri bilan o'ralgan bo'lsa, u ushbu erdan foydalanish turiga aylanishi mumkin.

Joylashgan joyi: shaharsozlik va inson faoliyati erdan foydalanishning o'zgarishi asosiy omilidir. Shunday qilib, shahar tumanlarida joylashgan erdan foydalanish xujayralari shahar atrofidagi tumanlarga qaraganda kattaroq faoliyatga ega.

Binobarin, erdan foydalanish xujayralari faoliyati quyidagicha ifodalanishi mumkin

qaerda men er - bu hujayraning holatiga bog'liq bo'lgan Faoliyatning boshlang'ich qiymati P - bu atrof-muhitga nisbatan eng ko'p erdan foydalanish turining nisbati L - bu joylashuv bilan bog'liq parametr va shahar joylashuvi faktori deb ataladi.

Shaharlarning joylashish koeffitsienti (L) 8-rasmda ko'rsatilgandek to'rt omilga bog'liq: (a) tabiiy omil, Yantszi daryosi va Xan daryosidan masofa (b) harakatlanish koeffitsienti, asosiy yo'llardan masofa (c) biznes omil, asosiy tijorat tumanlaridan masofa va (d) turar joy koeffitsienti, asosiy turar joylardan masofa. Shuning uchun L quyidagicha belgilanadi

qaerda D men yuqoridagi omillarni ifodalaydi a va b parametrlar.

(5) tenglamaga binoan L (8) -rasmdagi kabi illyustratsiya qilish mumkin edi. 8-rasmda L ULMG ning ikkinchi darajali panjarasi bilan bir xil darajada va katak o'lchamiga ega bo'lgan raster xarita sifatida ko'rsatilgan. Rastr xaritasida har bir piksel qiymati 0 dan 1 gacha bo'lib, inson faoliyati darajasini ko'rsatadi.

Shahar ko'llarining maydoni o'zgarishi chiziqli emas. CA qatlamining noaniqligini yaxshilash uchun erdan foydalanish katakchasining o'zgarishi yoki o'zgarmasligini aniqlash uchun tasodifiy chegara sharti kiritiladi. Boshqacha qilib aytganda, agar Faoliyat tasodifiy chegaradan kichik bo'lsa, uning holati o'zgaradi.

Agar erdan foydalanish katakchasi o'zgarishi kerak bo'lsa, u qaysi erdan foydalanish turini aniqlash uchun o'tish funktsiyasiga kiradi. O'tish funktsiyasi ruletka usuliga asoslangan. Uning ehtimolligi 3-jadvaldagi kabi taqsimlangan.

Agar o'tish funktsiyasi T (P) bilan ifodalangan bo'lsa siz , P n , P nr , P r ), erdan foydalanish katakchasining o'tish qoidasi (7) S land t & # x2b 1 & # x3d f Activities land S land t, & # x2009 P, & # x2009 L, & # x2009 TP u, & # x2009 P n, & # x2009 P nr, & # x2009 P r,

qaerda S er erdan foydalanish holati xujayrasi t - takrorlanishlar soni.

Ko'l hujayralarining o'tish qoidasi

Yuqorida aytib o'tganimizdek, amaldagi mahallalar soni (N) ko'l xujayrasi shaharlarning rivojlanishi va inson faoliyatiga qanchalik sezgirligini ko'rsatishi mumkin. N kattaroq bo'lsa, hujayra qanchalik sezgir bo'lsa va Aktivlik shunchalik katta bo'lsa. Shunday qilib, N ga ko'ra, ko'l hujayralarining sezgirligi ushbu jadvalda 4-jadvaldagi kabi to'rt darajaga bo'lingan.

4-jadvalda ko'rsatilgandek, N ning nolga tengligi ko'l hujayrasi nihoyatda barqaror ekanligini ko'rsatadi, chunki u ko'l ichida joylashgan. Agar N beshdan katta bo'lsa, demak, ko'l xujayrasi odamlarning ishiga juda sezgir bo'lib, keyingi davrda o'zgarishi uchun qo'shnilarning yarmidan ko'pi erdan foydalanadigan hujayralardir. 9-rasmda ko'l hujayralari sezgir darajadagi va befarq darajadagi ularning sezgirligi barqaror darajadagi hujayralardan kattaroq va juda sezgir darajadagi hujayralardan kichikligini anglatadi.

Ko'l xujayrasining mahallasi ko'l bo'yidagi odamlarning harakatlarini anglatadi. Oldingi tadqiqotlarimizga ko'ra, agar ko'l bo'yida erdan foydalanish o'zgarishi kerak bo'lsa, ayniqsa qishloq xo'jaligi erlari va rivojlangan erlarga aylantirilsa, ko'l maydoni kamayishi mumkin [42]. Shuning uchun ko'l xujayrasi & # x2019 faoliyati nafaqat mahalla ahvoliga bog'liq, balki mahallaning o'zgarishi bilan ham bog'liq bo'lishi kerak.

Yerdan foydalanish hujayralari singari, shahar tumanlarida joylashgan ko'l hujayralari ham shahar atrofidagi tumanlarga qaraganda faolroq bo'lishi kerak.

Shuning uchun ko'l xujayrasi & # x2019s faoliyati quyidagicha ifodalanishi mumkin

(a) Tabiiy omil (b) transport omili (c) biznes omili (d) turar-joy omili (e) L.

Erdan foydalanish hujayralarining dastlabki faoliyati.

Ko'l hujayralarining dastlabki faoliyati.

Ko'l hujayralarining sezgirligi.

Tenglamada (8), agar ko'l hujayrasi shahar tumanlarida joylashgan bo'lsa, L ko'l hujayrasida etakchi rol o'ynaydi & # x2019s faoliyati. Shahar atrofidagi tumanlarda qishloq xo'jaligi erlari ko'paymoqda va L kamayib boradi, shuning uchun P a ko'l hujayralarining etakchi omiliga aylanadi & # x2019 faoliyati. Demak, (8) tenglama ko'l xujayrasini aks ettiradi. Ham shahar, ham shahar atrofidagi tumanlarda faoliyat.

Agar ko'l xujayrasi & # x2019s faoliyati tasodifiy chegara qiymatidan kichik bo'lsa, u o'tish funktsiyasi dasturiga kiradi T (P) siz , P n , P nr , P r ) keyingi davrda qaysi erdan foydalanish turini bo'lishini aniqlash.

Shuning uchun, ko'l hujayralarining o'tish qoidasi (9) S ko'l t & # x2b 1 & # x3d f S ko'l t land & # x2009 S land t & # x2b 1, & # x2009 N, & # x2009 L, & # x2009 TP u, & # x2009 P n, & # x2009 P nr, & # x2009 P r, bu erda t - takrorlanish soni.

(7) va (9) tenglamalarda bo'lgani kabi, hujayra holatining o'zgarishi yoki bo'lmasligi uning faoliyatiga bog'liq. T (P) o'tish funktsiyasi siz , P n , P nr , P r ) ko'l hujayralari yoki erdan foydalanish hujayralari qaysi erdan foydalanish turini belgilaydi. (9) tenglamada bo'lgani kabi, S land t & # x2b 1 S ko'l t & # x2b 1 uchun zarur bo'lgan o'tish shartidir. Shuning uchun 10-rasmdagi kabi o'tish davrida ikkita faza mavjud.

CA qatlamining oqim diagrammasi.

(4) va (8) tenglamalarda shahar joylashish koeffitsienti (L) mavjud bo'lsa-da, CA qatlami hali ham inson faoliyatini to'liq aks ettira olmaydi. Shu sababli, CA qatlamida DULAEM-da MAS qatlami kiritilgan.

Shahar ko'llarining o'zgarishi bilan bog'liq uchta odatiy agent mavjud: hukumat, ko'chmas mulk ishlab chiqaruvchisi va rezident.

(1) davlat agenti. Shahar ko'llarini rivojlantirish va ulardan foydalanishda hukumat etakchi rol o'ynaydi. U shahar iqtisodiy rivojlanishini va ekologik muhofazani hisobga olishga qaratilgan. Odatda ko'l bo'yida erning qiymati yuqoriroq bo'ladi, shuning uchun bu erda inson faoliyati va LUCC tez-tez uchraydi. In order to isolate lakes from human activities, the government usually plans a greenbelt and wetland around the lakes. For instance, in Wuhan, East Lake is the largest lake in the urban districts. So there are many human activities around East Lake such as real estate development and aquaculture industry. On the other hand, the government attaches great importance to the ecological protection of East Lake through strict planning and supervision.

Therefore, in the MAS layer, the behavior of the government agent is summarized as that the strength of governmental supervision for lakes and lands gradually decreases with distance from the center of city [ 43 ]. The impact of the government agent on Activity is shown as (10) Activity = Activity ∗ IF gov , where IFgov is the impact factor of the government agent and it is from 0 to 1. IFgov is zero in the center of city and gradually increases to 1 with distance from the center of city.

(2) The Real Estate Developer Agent . The development of real estate is one of the primary causes for lake shrinkage, because filling in lakes can increase the land available to build houses so that the developers can achieve more economic benefits. However, filling in lakes is bound by the cost and earning of developers. As shown in Figure 11 , in the urban districts, the price of houses is high, but filling in lakes is almost impossible because the governmental supervision is strong here. In the districts away from the city center, the developers might fill in lakes more easily than in the urban districts, but it is not cost-effective for them because of lower house prices. Therefore, filling in lakes by the real estate developer agent usually occurs in the junction zone between the urban and suburban districts. South Lake of Wuhan, for example, was surrounded by agriculture land in the early 1990s. As the city expanded constantly, South Lake was almost surrounded by developed land by 2002. In the meantime, the area of South Lake reduced by 48.4%, and the reduced region had been nearly transformed into residential or commercial land.

Filling in lakes by the real estate developer agent tends to gather around the junction zone because of house prices and governmental supervisions.

Therefore, the impact of the real estate developer agent on Activity is expressed as in the following equation: (11) Activity = Activity ∗ IF deve , where IFdeve is the impact factor of the real estate developer agent and it is greater than 1. The more close IFdeve is to the junction zone, the greater it is.

(3) The Resident Agent . In some cases, the lake area had been occupied by the individual behaviors of residents, such as farming and fish-farming. These individual behaviors generally happen away from the city center because of the government and the real estate developers. Many lakes in the suburban districts, such as Wu Lake and Qingling Lake, had changed into ponds or paddy fields.

Therefore, the impact of the resident agent could be shown on the Activity as (12) Activity = Activity ∗ IF resi , where IFresi is the impact factor of the resident agent and it is greater than 1. IFresi is close to 1 in the urban districts and is increasing in the suburban districts.

(4) Interactions Between Each Agent . As shown in Table 5 , the government agent has the highest priority level and strength so that developers and residents must be subjected to governmental regulations. The resident agent has the lowest priority and the weakest strength, because the behaviors and decision-makings of residents should be more random than the developers’.

Relationships between the government agent, the developer agent, and the resident agent.

The priority levels of the agents determine the spatial distributions of their impact factors. The government agent has the highest priority level and supervises lakes mainly in the urban districts. So the developer agent develops real estate only in the junction zone between the urban and suburban districts because of governmental supervisions. The impact of the resident agent is mainly reflected in the suburban districts, because its priority level is the lowest.

(5) The Urban Border and Impact Factors . As mentioned above, it is necessary to determine the junction zone between urban and suburban districts. Accordingly, we proposed a border called the urban border. The urban border is a kind of geographic boundary rather than an administrative boundary. It can represent the actual situation of urban expansion. The urban border divides the city into the urban land-based regions and the agricultural land-based regions. So, extracting urban land and agricultural land from remote sensing images is the most direct approach to determine the urban border.

In this paper, the urban border is acquired by the urban location factor ( L ). According to Figure 8(e) , a series of isolines could be generated as in Figure 12(a) . Then the Landsat image of Wuhan in 2002 is overlapped with these isolines. As shown in Figure 12(b) , urban land in the Landsat image is nearly within the isoline L  =𠂐.7. Therefore, in the MAS layer, the urban border is defined as follows: (13) B = L L = 0.7 , where L is the urban location factor.

(a) A series of isolines from L . (b) The urban border is the isoline L = 0.7 .

On the basis of the urban border, the impact factors of three agents are simplified as in Figure 13 , where B is the urban border and L is the urban location factor.

The impact factors of three agents.

(a) (b) (c) 2.3.3. Integration of CA Layer and MAS Layer

In the CA layer of DULAEM, the transition rules of land use cells and lake cells depend on their Activities as shown in equations ( 4 ) and ( 8 ). On this basis, in the MAS layer, the impact factors in Figure 13 are added to Activities of land use cells and lake cells as in the following two equations, respectively: (14) Activity land = I land × P × L × IF gov × IF deve × IF resi , (15) Activity lake = I lake × α 1 P c + α 2 P a + α 3 L × IF gov × IF deve × IF resi , where Activityland and Activitylake are the Activities of land use cells and lake cells, respectively I land is the initial Activity of land use cells, which depends on its cell state I lake is the initial Activity of lake cells, which depends on N P is the proportion of the most land use types in the neighborhood P v is the proportion of the valid neighborhoods that have changed their states in this period P a is the proportion of agricultural land in the neighborhood L is the urban location factor defined as in equation ( 5 ) α 1, α 2, and α 3 are undetermined coefficients, and α 1 +  α 2 +  α 3 =𠂑 IFgov, IFdeve, and IFresi are the impact factors.

Meanwhile, the CA layer and MAS layer are integrated on the ULMG. The first-level grid of the ULMG could limit the extents and borders of the cell space and the agent space. The second-level grid is a carrier of cells and agents. In other words, a grid in the second level is a cell in the CA layer, an agent unit in the MAS layer, and a pixel in the remote sensing images.

In this paper, the accuracy of DULAEM is evaluated by the global relative error (GRE) defined as (16) GRE k = ∑ i = 1 N A i k − A i / A i N , where A ik is the area of the i th lake in the k th period A men is the actual area of the i th lake in 2002 N is the number of lakes and ( A ik – A men )/ A men is the local relative error (LRE) of the i th lake in the k th period.

3. Results and Discussions 3.1. Evolutions Based on CA Layer

In equations ( 8 ) and ( 15 ), there are still three undetermined coefficients: α 1, α 2, and α 3. In order to determine these coefficients and assess their effect on the GRE, we tested 16 groups of typical cases (as Table 6 ). For each group of α 1, α 2, and α 3, we ran DULAEM without the MAS layer 100 times and calculated the GREs according to equation (16).

Sixteen typical cases of α 1 , α 2 , and α 3 .

The mean, variance, and optimum of GREs in 100 simulations on each group are shown in Table 7 . The statistical results indicate that the GREs in Groups K, M, O, and P are less than those in the other groups. Their mean values are all close to 0.40. Their optimums are all less than 0.40, and K  <  P  <  M  <  O .

Figure 14 shows the iterations of Groups K, M, O, and P by the CA layer. As shown, the fastest group is Group O, which can reach steady value before the 80 th generation. Group M and Group P reach steady state around 90 th generation and 110 th generation, respectively. Group K needs at least 130 iterations to reach steady state.

The iterations of Groups K, M, O, and P.

According to Group O, farm works whose coefficient is α 2 and the urban location factor whose coefficient is α 3 are the primary cause of the evolution of urban lakes. If α 2 equals α 3, the GREs are less (see Groups K, O, and P). On the basis of α 2 and α 3, an appropriate α 1 could reduce the GREs further (see Groups K and P). In addition, the number of iterations to reach steady value depends on α 2 and α 2. The greater α 1 is and the smaller α 2 is, the more iterations are needed.

Figure 15 shows the spatial distributions of LREs under the best GRE of Groups K, M, and P. As shown, three distributions are similar: the lakes located in the urban districts are almost overfitted namely, their LREs are less than zero and the lakes located in the suburban districts are almost underfitted namely, their LREs are greater than zero. Some suburban lakes in Figure 15(b) are also overfitted because α 2 in Group M is greater than that in Groups K and P.

The spatial distribution of LRE of Groups (a) K, (b) M, and (c) P.

Meanwhile, it is necessary to improve model by MAS layer.

3.2. Evolutions Based on DULAEM

By comparing above GREs and iterations, we adopted Group P ( α 1 =𠂐.33, α 2 =𠂐.34, and α 3 =𠂐.33) in the CA layer and introduced MAS layer to run DULAEM 100 times.

3.2.1. Evolutions of Lake Area

GREs and LREs in 100 simulations were calculated according to ( 16 ).

Figure 16(a) shows the statistical result of GREs. A contrast between Figure 16(a) and Table 7 indicates that DULAEM could improve the GRE more than a pure CA layer. The mean of GRE is less than 10%, up to 0.091301. The optimum of GRE is 0.076477. At the same time, DULAEM is faster due to MAS layer so that it reaches steady value before the 70 th generation (Figure 16(b) ).

(a) The statistics of GREs in 100 simulations. (b) The iterations of DULAEM.

However, according to Figure 17 , although the GRE can be kept below 10%, there are a few lakes with larger error in some cases. For instance, as shown in Figure 17(b) , the minimum of the mean of LRE is less than 20%. In Figure 17(d) , the LRE could be fluctuant sharply so that the difference between maximum and minimum is even more than 150%.

(a) LRE of each lake. (b) The mean LRE of each lake. (c) The mean of the absolute value of LRE of each lake. (d) The difference between maximum and minimum of LRE of each lake.

Figure 18 shows the spatial distribution of LREs. The lakes with larger LRE tend to gather in the suburban districts irregularly. They were all small lakes in 1991 and had larger loss rate during 1991 and 2002. Thus, the lake area change in the suburban districts is more random and complex than that in the urban districts.

The spatial distributions of (a) LRE, (b) the mean of the absolute values of LRE, and (c) the difference between maximum and minimum of LRE.

An overlap analysis in Figure 19 shows that the simulation result by DULAEM well matched with the real lake areas extracted by Landsat images. However, if a lake had been filled in over 50%, there could be a big error between the real lake area and the simulation result.

(a) Urban lakes extracted from Landsat images in 2002. (b) Urban lakes evolved by DULAEM. (c) Overlap of urban lakes in (a) with those in (b).

(a) (b) (c) 3.2.2. Evolutions of LUCC

The evolution of LUCC is hard to be assessed by traditional methods such as the Kappa coefficient, because there is only a 100 m buffer at the lakeside. For this reason, the proportion of each land use type in the buffer of each lake is contrasted with its real values extracted from Landsat images. For instance, the proportions of developed land evolved by DULAEM have similar tendency to the real values in Figure 20 . Particularly, in the buffers where the proportion of developed land is greater than 40% in reality, the simulation result is larger than the real value. On the other hand, the simulation result is smaller than the real value in the suburban buffers. The polarization like in Figure 20 could be caused by the transition function T P u ,   P n ,   P n r ,   P r in the CA layer.

The proportion of developed land in 100 m lakeside buffers.

This paper proposed a dynamic model based on a special geographic information grid (ULMG) and MAS-CA model. A case study on Wuhan, China, proved that the model is effective for urban lake area evolutions.

The ULMG is a two-level grid that has the advantages of both vector model and raster model. It is designed for running MAS-CA model originally. The structure of ULMG is efficient for massive amounts of grids. It is available for the data management of large-scale, discrete spatial features such as urban lakes.

The CA layer of DULAEM indicates that urban lake area changes have correlations with their activities, which depends on locations and surrounding environments. For the lakes in the center of city, broad greenbelt landscape of at least 30 meters is necessary.

The MAS layer of DULAEM indicates that the area changes of urban lakes are also related to governments, real estate developers, and residents. These three agents have different actions in extent, strength, and priority. The government must pay more attention for the urban lakes, especially in the rural areas, and take some rules for real estate developers and residents.

DULAEM based on the ULMG and MAS-CA model reflects the natural factor and human factor for urban lake so that it can show the dynamic process of lake area change. Therefore, it would be significant for the policy-making of lake protection and the optimal configuration of land resources in the lakeside.


GIS and census data: tools for library planning

Purpose &ndash This article seeks to demonstrate a technique for using a Geographic Information System (GIS) to analyze US Census data to better understand potential library users and improve library service planning. Design/methodology/approach &ndash A GIS was used to link variables such as age, race, income, and education from the 2000 US Census with service area maps of two proposed branch libraries. Thematic maps were created for each of the census variables to display demographic information about potential library users within a three‐mile radius of the proposed libraries. Findings &ndash The GIS maps and their associated attribute data enhanced the ability to analyze and compare the demographics of potential users in the two library areas and identify significant differences. The data on age, race, education and income for residents in the two areas were combined with known library use indicators to help plan library services with the potential to attract different populations in the local community. Originality/value &ndash Provides practical information about downloading US Census data into a GIS to be able to present demographic data about potential library users both visually and quantitatively.

Journal

Library Hi Tech &ndash Emerald Publishing

Published: Jun 19, 2007

Kalit so'zlar: Geographic Information Systems Census Information services United States of America


Videoni tomosha qiling: ЖИГА НОМЕР ОДИН. 2106 НА СТИЛЕ. СУПЕР ТЮНИНГ 2106. SUPER TUNING QILINGAN 2106