20170427 18391700 TRUE CreateFeatureclass "Database Connections\Connection to MAPS as PORTGIS.sde" Polls # polls_8_06_Layer SAME_AS_TEMPLATE SAME_AS_TEMPLATE "PROJCS['NAD_1983_StatePlane_Maine_West_FIPS_1802_Feet',GEOGCS['GCS_North_American_1983',DATUM['D_North_American_1983',SPHEROID['GRS_1980',6378137.0,298.257222101]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Transverse_Mercator'],PARAMETER['False_Easting',2952750.0],PARAMETER['False_Northing',0.0],PARAMETER['Central_Meridian',-70.16666666666667],PARAMETER['Scale_Factor',0.9999666666666667],PARAMETER['Latitude_Of_Origin',42.83333333333334],UNIT['Foot_US',0.3048006096012192]];2801856.72998744 171494.725729625 7812.49999272404;0 100000;0 100000" # 0 0 0 "Database Connections\Connection to MAPS as PORTGIS.sde\sde.PORTGIS.Polls" Append polls_8_06_Layer "Database Connections\Connection to MAPS as PORTGIS.sde\sde.PORTGIS.Polls" TEST "Database Connections\Connection to MAPS as PORTGIS.sde\sde.PORTGIS.Polls" FeatureClassToFeatureClass "G:\GISdata\Districts & Boundaries\Voting Districts.mdb\datasets\polls_8_06" "Database Connections\Connection to MAPS as PORTGIS.sde" Polls # "AREA AREA VISIBLE;PERIMETER PERIMETER VISIBLE;POLL_PT_ POLL_PT_ VISIBLE;POLL_PT_ID POLL_PT_ID VISIBLE;NAME NAME VISIBLE;ACAD_LAYER ACAD_LAYER VISIBLE" SAME_AS_TEMPLATE SAME_AS_TEMPLATE # 0 "Database Connections\Connection to MAPS as PORTGIS.sde\sde.PORTGIS.Polls" CalculateField "POLLING LOCATIONS" ACAD_LAYER 'T-POLLING PLACE' PYTHON_9.3 # CalculateField "POLLING LOCATIONS" ACAD_LAYER 'T-POLLING PLACE' PYTHON_9.3 # Server=GISDEV; Service=sde:sqlserver:GISDEV\MSSQLSERVER_2014; Database=sde; User=PortGIS; Version=sde.DEFAULT ArcSDE Connection 1.0 FGDC CSDGM Metadata Political Voting Polling Elections Precincts City of Portland Polling Locations. These are the locations where citizens vote during local elections. Polling Locations /9j/4AAQSkZJRgABAQEAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCADIASwDASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3kdab 0Z6UnBph5z70hjIeWJ6inkbj9KcoCp+FMcnafegBrPhxjpUv8OagPyEdyayNR8QRWsZhhIknHBPZ aqMXJ2QDfEF+ILQW6N+9l5OOy1ydPllknlaSVizt1JplepRpezjbqQ3cKKKK2EFFFFAHS2XiWKKy RLlZHmTj5R1FJN4sc/6m2A93auborn+rU73Hc1ZvEOozHiUIP9gYqhLd3M5zLPI592qGitI0oR2Q XA8nJ60UUVoIKKK19I0OTUCJZcpB692+lROcYK8hjvD5vTdgQsRbg/vC33R/9eutPp3J4oS3igt1 giQIg7CnNjAHcdK8qrPnldKxSJl4HNNJyaFbcPcdaR221mMjkbaKbGmXLdh+poYGRgB+JqfpQAUU UUAIQGBBAIPY1iX+gq+ZLTCt1KHofpW5RTTsKUVJWZwkkbxSFJFKuOoIptdpeWEF6m2VfmHRh1Fc zfaXPZEsRvi7OP61alc5p0nHVFGiiimZBRRRQAUUUUAd4elNB4px4FMzggVkd49fujNNOMEsaM8g e9I3Q+goAiLEncR9BXJazpxt5PtEY/dyE7v9k11hcKhdjgdTWbe3ttNbPAieYHGCTwBVQreylzMm coxWrOYtLVbkOzMQFOMCmXNu1s/coejH+VakEC28excnuSe5pLsqLWQuARj9ayWOqe35lqn0OFVn z6bGNRUVzcQWNm91dS+XChAJ6kk9gO9TfKUSSN1eORQ6MvQg17inG/L1O0SiiirEFFFFABRRRQAU UUUAaui6UdQuA0g/cKef9r2rtlVY1CqAABgAVS0kIuk25jQAFM4HrV3oBnrXkVqjnK7LQds0KMtu PbpS4yeaVvumshkMTYlkNByzkdT/ACpsYzOT0GOBUyqF6d6ABVCrgUtFFABRRRQAUituGfekb5sp n607GOlABTZGRY2aTGwDnPpTq5zXNS81jaQn5FPzkdz6U0rkykoq7Mu6eKS6keBNkZPCj+dQ0UVo cbd3cKKKKBBRRRQB3h5Wohy/sBUpGVI6VFwowOgrI7xXYB19BTWyzkfw4/OjaXKsDwKjuLiO1jEj k8cAdyaTE3Yi1FkjsJVPV1IX3JrCqW4uJLmXfIf90DoBUVcNaopvQ86vUU5aBWfqUn3Ih3+Y1oVk XxJvGz2UCt8BBTrq/TUKCvMoXdjZ6hCIL62S5hDbvLckDPrwRUkUMVvBHBBGI4Y1Coi5woHbmn0V 9Fyq/NbU7woooqhBRSA57EUtJO+wBRRRTAKKK1NAs1vNTXzOUjXeR6+n86iclCLkxnWadE8Om20Z 6qgBFXOnJNAoYZArx276lijkZqMsehqRTxg01xgUgGKpDlhzkUoYmQjt2pEYkdO/Sj7nJ+8xwKAJ KKKKACmucRkgZPYU6igBqLtyT1PJp1FV728jsrdpXPPRR6mgCnrOo/ZIfKjP75x/3yPWuW69etST TPcTNLIcuxyajrRKxx1J8zCiiimQFFFFABRRRQB3mRyT2qEtvbH40SgySKoPB6/SniPb0/GsjvFT Aj9iaxdUhnLiQgtCo7fwn1NbIweueO1ZGq3ZeQ26cJxvPr7VnVtyO5lWtyO5m0UUV555gVm6lHiV JR0YbT/T+taVQ3cXm2zr3HIrfDVfZVVIunLlkmY1FFFfUHohUlvCZ51jHQ8n6UxVZ2CqMseAK3LO 0W1T1dvvGuPGYlUYcq3ZSVzJvAFu5FHAHA/KoK3rq1jnhf8Adr5hHDY5zWDyDg8EdanA4hVIcvVB JBRS4JGQDj1xSV2qSeiYgrofCcZN1cS44VAv5n/61c9XZeGIfL0wyd5HJ/D/ADmsMVK1O3cEbOfm wKCeCR2paBwuK8wsaDyaVvmwKYvzHjgA0/ocgZoAAAtRrlpmJ6DpTzuY9MfWhVCjHf1oAdRRRQAU UVR1DU4bFMH55T0Qf1oE3bVk91dxWcJklbA7Dua5K+vZL6fzH4UfdX0FMurqW8m8yZsnsOw+lQ1o lY5qlTm0WwUUUUzIKKKKACiiigAooooA7xVCjig0mSaCDWR3lDUbs20YEePMY4Ge3qawySzFmJLE 5JPetHWQBPCB/db+lZ1cWIk+axwYmT5uXoFFFFYHMFFFFAGLcx+VcOvY8ioq0dSjyiygfdOD9DVW 0g+0XAQ/dHLfSvocNiV9W55dD0KMuaKL+m2uxPOcfO3T2FaFHSivDq1JVJOcup0hVaewhuJRI+4H uAcZqzRUxnKLvF2AZFEkMeyNcL6U17aGT70Sn8Kloo5ne9xlJ9Lt2+7uT6H/ABrotEUR6csIOfLY rn1rKq9pUuy5eMnh1yPqP8/pXRSrTk+WTuhWNjoaRuAaXtzTT83HbvXSIEGEH0p1FFABSH1paKAC iisDXdb+zZtbZv3pHzt/d/8Ar0pSUVdgP1rXlswYLbDzdz2X/wCvXPeYZv3jMWLckmqJJJJJJJ6k 1JbvtfYejdPrWVGteVmYVU2rlqiiius5wooooAKKKKACiiigAooooA7veBx3qrdahDBuUuPMx90c mufk8Uzb/wBzboq/7XWqDapJPfNNKAFfAIHbtWE21G6N54iKXu7mlNPJcOHlPI6Adqjoorzm23dn BKTk7sKKKKQgooooAbIgkjZD0YYqrpUZVpiw+YHbVymWsqGWaIfeU5P41rCpJQlBbM6sI/eaLVZ+ s6zaaFY/a7x9qbgoGQCSfTNaFZWtx6dNDHFqNnDdoWyI5UDAe+DRTjzSStc727Iv2tzFeWsVzA4e KVQysO4qas4TQWukZ09Y1jiACIowF56Yp1nqPnBVnXy3PQnoabpSacktELmWxfooorIoKVWaN1kT 7ynIpKqzX8ED7GJJ74GcU1e90I6q2uIrqISRurDvg9Kmrzv7U8V001qWhz0CnFbVl4okTC3ce8f3 1612Rrxe5NzqaQNzg8GqsGq2l2uYZVJ/unqKnUM7BjwB0ra9xktFFBOBk0AZus6kNOtCV5lfhB/W uGZmdizElicknuavavfG/v3kB/dr8qD2qhXFVnzO3QlhQRn60UVlsItwyeZGD3HBFSVSjfy5N38J 4NXa9OlU543OWceVhRRRWhIUUUUAFFFFABRRRQBRooorMwNa0n8+H5vvrw1WKyrBit1tHRl5/CtW vOqx5ZWQmFFFFZiCiiopbiKDHmNgmi1wJawZbuW21KaSIjO4gg9xWzFcRTD5GBPp3rnLht1zK3qx Nd2CgpTakuhtRunc3bbWIJRiX92/6GsW8uTdXLSnp0UegqCiu+lhoU5OUTqlNyVmPilaJwQTtJG5 ezDPSptF0LU49ZuNRvNSW4tbnn7OYdpUg5HOe1Vq6bS7prm1y/31OD71jjbxjeKKpPoXaKKK8k6A OcHHWubkBErhvvbjmt+adIIXlc/Kg5xXPFxIS46Mc0yZBRRRSIFVmRgykqw6EHBFbFl4ku7bCzYm Qf3vvfnWNRVxnKOwzu7LWrO9ACybH/uvxTNfvPsuluFPzy/Iv49f0rh++akkuJpkVJJWdV+6GOcV t7fSz3C5HRRRXMIKKKKADrVi2kyuwnlenuKr0AlWDDqK2o1OSXkTOPMi/RSKwdQw6Glr0jlCiiig AooooAKKKKAKNFFFZmBc01MyyP6AAfj/APqrSqrp6bbUH+8Sf6VarzqsrzbEwooqldXE1tMCMNG3 QH1qEr6AlcuMwRSxOABmsJ3MjlmOST3qzdXazwqo3K2eRVWtqcbas0hGwhO35umO9UhnAz1qDxDr kGhWtq0kEk7XDkMEVjtQdTwOvNWpAqyMEOV7GvRwitdm6i0rjaKrXuraVplzY217dqk14flG4DaM kZIPXkdqtyIY5XQ9VJB/CutSTdkNpobWtoUmJpYvVd1ZNW9Nd475GRSwGdwH0rPEQ56bSKg7SudR 061Xe57RDcfXtULs8p+c8f3RRXHRwPWp9w54jpEyZi8jzLI5w5O4Doaiij8pNmcgdKqa3r+n6Jql nZ3W4y3bZ4B+VScA8e+avuArkA5APWuStBwk+xom2kxKKKKwGFFFFABRRRQAUUUUAFFFFABRRRQB LbvtfYeh5H1q1VA/rVuGTzIwe44Nd+Hqcy5X0MKsbO5JRRRXSZBRRRQAUUUUAUaQ9DV2XTZoxlCH HtwaqpGXmWPBBJwfaseeLV0Y2NiAAQR46bRUlAAAAHQUV5jICmuiyIVcAg06igDDmj8qd4+oU8Gm Vp3dn5zeYhw+OQehrNdWjYq42ketdMJJo2i7ofHPLDny5GXPBwetV/JjJyUFSUVabWxd2MeKF1+e 3hcgYVnjBK/Q9qqAkgE9atykiJiPSqvSu3CX1ZSbsFPimeCQOhwf51JBaS3OSnAHc9/aoXRo2KuM MOorsH5G9bXC3MW8DBHBFTVS0sYtjkdWNXaDF7la7aSNN6MQOjY9KoVrSKHjZT0IrJFeXj4Wkpdz poyurBRRRXnmwUUUUAFFFFABRRRQAUUUUAFFFFABTo38uTd/CeDTaOtXCTjK6E1dWL9FQW8mV2Hq OnuKnr1IyUldHK1Z2CiiimIKKKKANGVyMIv3j+lQrbxrJ5gX58Y3E09FIyW5Y8k06vIcuxg5BRRR UkhQSAMngUVlXl0ZnKIf3Y4471UY8zHFXLT6hCjYG58d1HFUri6a442Kq/TJqCitlBI1UUgAwMUV Q1STXVms/wCx7SKW3Rt1yXnRMjI4weSMZ6VoNgsSowPStWrK5o42SY0jcCD3qngqdp6g8+9aMED3 EojjHPcntWomi2wYNKWdu/YVtQqcj12EnYq2VwssWFj8sDgDsameCKRgzoGI6E1kaH4hbWNV1HTG 0y5torcFreSSBkVtp2kZIwSc5GO1bSnKg1206nOE4uLFAAGAMCs3X9VbRtHlvEt5biQEKkcUZckn uQOcVpVTe/dJv3J4Xv60q1RU4XbCnG8ivFqB1DRrO8WN4RcJl45AVdGBwQQenTP0NR1JPPJcSGSQ 5Y1HXj1qrqSuzrUUtgoqW2tpbudYYV3O36V2NhoNrbW4WZFllP3mP9KUKTmVY4miu/8A7JsD/wAu sf5VQ1Lw7BPFutFEUq9uzVbw7S0YWOPop8sTwStFKpV1OCDTKwEFFFFIAOEieV2CRoMszdAKUjAU 5BDDII6EVWv7C31Wwexu2nWB2DMYH2NkZxz6c0+1tYrGzhtIGlaGFdqGVtzY9zV2jy36jJaKKKgQ UUUUAAJVgy9R+tXlYOoYdDVGpbd9r7D0PT6114apZ8rMqsbq5aoprSIn3mA/GojdIPuhm/DFdjlG O7MUm9ieiqpuJD0UL+tM8yQ/8tD+FYvEQRapSNtch2QnO3vTqRVCLj9T3prSxoMs6ge5rznq9Dje r0H015EjXc7BR71Ul1ayiBJnViOy8mse71hJ5AwRto+6Ola08PUnsi40pS6GldXvmAxxEhe7etU6 zm1F/wCFAPrVm1eWVS8mMH7oxXS8PKnG7NvZuKLFFFFZkhRRkZxmjBJwBknpQBs6RFtt2lI5c/oK 0ajhjEMCRj+FQKkrVCEwB2FVJrdIkeXzQiKCzbugHerlU9U0+PVdOmspZJI0lG0tG2GH0NVGTi7o a1eplyySS24ngmWW1fpJGcj86q1raFoVvoGnmyt5p5otxbM77m596r6lbR28qmMYV+3YVjiOaXvN m8JRT5UUans7Sa+uBDCuWPU9gPU0+wsJtQuBFEOP4m7KK7fT9Ph0+3EUQ5/ibuxrKlS5tXsbpDNM 0yHToAqgNIfvP3NXs0vSkrtStoihvQ/WnUnvTZG2kAdaAMXxDpX2qE3UK/vox8w/vCuQr0vGRzXn NzH5N1LGeqsRXLiIpe8SyKiiiuYQUUUUAFFFFABRRRQAUEZ60UUwEAA7UtFFABRRRSAypL+7l+/c SfgcfyquzFjliSfc0lFfTRhGOysNJLYKKKKoYVN9rn248zj2AFQ0VMoRl8SE0nuSGeU9ZG/Om+ZJ nPmP/wB9Gm0UKnBdAshQ7BtwY7vXNWIr+4iZWDAlTkZFVqKTpwlugcU90bun+IbkXSrdOHiY4J2g bfyrqwQRkcg15vXa6Jdm40yPfkMnyZI649K5MTSUbSic9WCWqNOip4LKe45AEaf3mHP5VaGkpj5p pM+2BXLYhUpMzqilsP7RKwDhs53f3RWv/ZUWcedL+Y/wq1b20dsmEByerHqafLfc0hRad2Ns7OGw tlhhXAHU92PqasYzSOMAmlHSqOkDz+HSheaCO9A+5mgBJDjIXrTPvOD6U8/3u9Ii8UAOrhNdi8rW Jx/eO7867hH3ZHcHFcp4qi238Un99OfwrGurwEzBoooriJCiiigAooooAKKKKACiiigAooooAKKK KAMGiiivqCgooooAKKKKACiiigAooooAu6Raw3mqQW9w5SN2wSO/tXq0FpBbwpFFEojQYUY6UUVx 4l+8kBNkCl7UUVzDEHWloooAKAKKKAHdqaRnjtRRQA084pw4Qn0oooArw58+TPoKxvFcWbWCUD7r 4J9sUUVFT4GDOUooorzyAooooAKKKKACiiigAooooAKKKKACiiigD//Z