20210301 16004400 1.0 FALSE SensitiveCommunities_UCB_UrbandDisplacementProject 002 -124.409591 -114.131211 32.534156 42.009485 1 Geographic GRS 1980(IUGG, 1980) Angular Unit: Degree (0.017453) <GeographicCoordinateSystem xsi:type='typens:GeographicCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.0.0'><WKT>GEOGCS["GRS 1980(IUGG, 1980)",DATUM["D_unknown",SPHEROID["GRS80",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]</WKT><XOrigin>-400</XOrigin><YOrigin>-400</YOrigin><XYScale>11258999068426.238</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>8.9831528411952133e-09</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><LeftLongitude>-180</LeftLongitude></GeographicCoordinateSystem> 20230926 12311400 20230926 12311400 150000000 5000 ItemDescription Esri ArcGIS 13.0.3.36057 SensitiveCommunities_UCB_UrbandDisplacementProject 1 -124.409591 -114.131211 42.009485 32.534156 Map of sensitive communities, or places where residents may be particularly vulnerable to displacement in the context of rising property values and neighborhood change <DIV STYLE="text-align:Left;"><DIV><DIV>Defining Sensitive Communities:Communities were designated sensitive if they met the following criteria: They currently have populations vulnerable to displacement in the event of increased redevelopment and drastic shifts in housing cost. Vulnerability is defined as:Share of very low income residents is above 20%, 2017ANDThe tract meets two of the following criteria: Share of renters is above 40%, 2017Share of people of color is above 50%, 2017Share of very low income households (50% AMI or below) that are severely rent burdened households is above the county median, 2017They or areas in close proximity have been experiencing displacement pressures. Displacement pressure is defined as:Percent change in rent above county median for rent increases, 2012-2017OR Difference between tract median rent and median rent for surrounding tracts above median for all tracts in county (rent gap), 2017Read in-depth methodology description here: https://www.sensitivecommunities.org/wp-content/uploads/2020/01/UDP_SC_Methodology.pdfOriginal data sourced from: https://www.sensitivecommunities.org/</DIV></DIV></DIV> PlaceWorks 2021, UC Berkley Urban Displacement Project HUB Housing Needs and Displacement Risks File Geodatabase Feature Class dataset 0 Simple FALSE 0 TRUE FALSE SensitiveCommunities_UCB_UrbandDisplacementProject Feature Class 0 OBJECTID OBJECTID OID 4 0 0 Internal feature number. Esri Sequential unique whole numbers that are automatically generated. Shape Shape Geometry 0 0 0 Feature geometry. Esri Coordinates defining the features. GEOID GEOID String 80 0 0 COUNTY COUNTY String 80 0 0 NeghTyp NeghTyp String 80 0 0 big_cty big_cty Double 8 0 0 tr_md12 tr_md12 Double 8 0 0 tr_chrn tr_chrn Double 8 0 0 tr_pchr tr_pchr Double 8 0 0 tr_ttn_ tr_ttn_ Double 8 0 0 tr_LI_c tr_LI_c Double 8 0 0 tr_VLI_c tr_VLI_c Double 8 0 0 tr_ELI_c tr_ELI_c Double 8 0 0 tr_LI_p tr_LI_p Double 8 0 0 tr_VLI_p tr_VLI_p Double 8 0 0 tr_ELI_p tr_ELI_p Double 8 0 0 t_LI_30_ t_LI_30_ Double 8 0 0 tr_LI_30 tr_LI_30 Double 8 0 0 t_LI_50_ t_LI_50_ Double 8 0 0 tr_LI_50 tr_LI_50 Double 8 0 0 t_VLI_30_ t_VLI_30_ Double 8 0 0 tr_VLI_30 tr_VLI_30 Double 8 0 0 t_VLI_50_ t_VLI_50_ Double 8 0 0 tr_VLI_50 tr_VLI_50 Double 8 0 0 tr_pch_ tr_pch_ Double 8 0 0 tr_chr_ tr_chr_ Double 8 0 0 tr_mdr_ tr_mdr_ Double 8 0 0 tr_pplt tr_pplt Double 8 0 0 tr_dq tr_dq Double 8 0 0 tr_rentgp tr_rentgp Double 8 0 0 tr_rntgpp tr_rntgpp Double 8 0 0 tr_pstd tr_pstd Double 8 0 0 tr_pvst tr_pvst Double 8 0 0 tr_mdrn tr_mdrn Double 8 0 0 tr_prnt tr_prnt Double 8 0 0 tr_rb tr_rb Double 8 0 0 tr_hshl tr_hshl Double 8 0 0 tr_pWht tr_pWht Double 8 0 0 tr_pBlc tr_pBlc Double 8 0 0 tr_pAsn tr_pAsn Double 8 0 0 tr_pLtn tr_pLtn Double 8 0 0 tr_pOth tr_pOth Double 8 0 0 tr_pPOC tr_pPOC Double 8 0 0 tr_pwlf tr_pwlf Double 8 0 0 tr_ppvr tr_ppvr Double 8 0 0 tr_pnmp tr_pnmp Double 8 0 0 tr_pfmh tr_pfmh Double 8 0 0 tr_POC_ tr_POC_ Double 8 0 0 t__VLI5 t__VLI5 Double 8 0 0 c_mdr12 c_mdr12 Double 8 0 0 c_mhhnc c_mhhnc Double 8 0 0 c_LI_vl c_LI_vl Double 8 0 0 c_VLI_v c_VLI_v Double 8 0 0 c_ELI_v c_ELI_v Double 8 0 0 c_LI_30 c_LI_30 Double 8 0 0 c_LI_50 c_LI_50 Double 8 0 0 c_VLI_3 c_VLI_3 Double 8 0 0 c_VLI_50_ c_VLI_50_ Double 8 0 0 co_rntgp co_rntgp Double 8 0 0 c_rntgpp c_rntgpp Double 8 0 0 c_pstdn c_pstdn Double 8 0 0 c_rVLI_50 c_rVLI_50 Double 8 0 0 c_pchrn c_pchrn Double 8 0 0 c_prntr c_prntr Double 8 0 0 co_pPOC co_pPOC Double 8 0 0 c_LI_pr c_LI_pr Double 8 0 0 c_VLI_p c_VLI_p Double 8 0 0 c_ELI_p c_ELI_p Double 8 0 0 v_LI_30 v_LI_30 Double 8 0 0 v_LI_50 v_LI_50 Double 8 0 0 v_VLI_3 v_VLI_3 Double 8 0 0 v_VLI_5 v_VLI_5 Double 8 0 0 v_VLI v_VLI Double 8 0 0 v_Rntrs v_Rntrs Double 8 0 0 v_RB30L v_RB30L Double 8 0 0 v_RB50L v_RB50L Double 8 0 0 v_RB30V v_RB30V Double 8 0 0 v_RB50V v_RB50V Double 8 0 0 v_POC v_POC Double 8 0 0 dp_PChR dp_PChR Double 8 0 0 dp_RntG dp_RntG Double 8 0 0 s1_VLI5 s1_VLI5 Double 8 0 0 s3_VLI5 s3_VLI5 Double 8 0 0 tier2 tier2 String 80 0 0 tier3 tier3 String 80 0 0 tier1 tier1 String 80 0 0 text text String 80 0 0 ppp_txt ppp_txt String 80 0 0 Shape_Length Shape_Length Double 8 0 0 Length of feature in internal units. Esri Positive real numbers that are automatically generated. Shape_Area Shape_Area Double 8 0 0 Area of feature in internal units squared. Esri Positive real numbers that are automatically generated. 20230926 /9j/4AAQSkZJRgABAQEAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCADIASwDASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD1qiii ugwCiiigAooooAKKKKACiiigArn9Uks47iYXT7C0iYZRzjAyM10FcvqulT6o8jwEMrTjOewA2n9a zq35fdVzowqpuqlUlyruF/qNwl7EtrC0ltImC8QJJHtjuKw3gkt7h9OvBiN+UZh90+oNdtZadFZ2 otkHzIM5J61h619jvr6GyeSSOdeRIVz+FYV6TUeZvXt+h6GX4uDqOlCHu21fXTZnOWETQXUwYYli RsZ7HpUFraSag/yDcobDsT0ro7m2S31Czd4CnnDy5GLZJJUjn69aujwstlciWz8wjbyDIAM/TFZ0 cPzS97odWMzTkp3ho5r120YirtQL6DFLTQW3ujrtZDginV7B8qFFFFAARkYNaGk3GM2rnleUJ7j0 qgFJ5xx606SMxlZYny6nK4qJK6Ki7M6Gioradbm3WVe45HoalrE2LNqAwZTn86sGNFUkjPHeqtqc TfUVdbBBHqKCXuc/PAQ0lvjkHen0703G4m12kqo6gdG9a0NQlSF4pihMi/d9DVOWaVbgXcafumAB x0+lccopSPWp1JSgnb/hzQ05QsO3CLIvDAEHNWdzl8hc44znrXPXEySPviYq5/hzyabbXRE8btIx RTzg1oq1tLGUsG53mnr2OppGG4YqI3UKxCRnAU9M1mXOskOBAAVHUkda1lOMdzjpYepVdoo1wex6 0rDI9+1Vra7jvEyhw46irKnIqk01dGcouLtJamVqU1zEoMZxGeuB3p2nSXMw3zP+7xx71fmiEiMp AKnqKLeJYoFjHRelZqD5730Oh1oOjy8qv3EUA4AHTrxT94HBBH0FPorU5TKooooLCiiigAooooAK KKKACiiigBrMQCFG58EhR3qvZI0MA84bS2ZAp9GOf61HHhNRmbcdwAKsT0B6j+VWOZomdiTtPy5H QD0/Kqgm3czqSVrdRywK+XlUFieM9hWXqWjpfi2mA2yRuNxHp/nFa7Sn5AoGWHU9KcibVIJzk5NX OEZrlZFGvOjPng7M5PWdV2XgtLi3CRKQwYcnPUH2rTbxBBb6a8shxIi8DrnPQ/SjxCscGnTTtbLM cBeRyMkAe9Y3h2xMErHUDH5/34bd3BMaf3sduc1yr2kauh6cnh54RNqzXnv3fl5l1Jo7vFwNm9+S V4B+o9ae5UDIQEnsG/8ArVka+bzTJ3u9JgFyk3zSK7/Kre3pn+lZml+I9Sut63empE6oWASQEnBA 6Z9609pFaOVjmhh6k1eMbo6j0+VRkc4bOP0pKqadem+tzIU2EHGKt10Q1V07nPUTjJxas0SRyshG DgVNLMAMo+eemKrKpY4FO/djoCx9+BQ7XEiWxuTBdlWP7uU8+zVvbT3IFc1ITIhU4APHy8Vq6bdG eAo5/ex8N7+9ZTi9zSLWxpxFVlXGfrV8KBWbH/rF+tadRYbZHLBHMuJEDAdM1SuoA9i6QqBjk46G r0ufKbb1xxWZa/aLPMc+DEf4ielRPtY2o3+JPboY8agzLuLY+6QO4PFaLaZaw6efIJGwbvrVsXVr FIB5KlSeHVc80iXUNxcvbqg2MMA4/OsoxSVmzrqVJymqkYtWMPJIxnikq1e2jWsuOqHoaq1zNNOz PWhOMo80di5pbbb5OTzxXRH5Wz2PWsC0lSyk3MN0jDgelaVjqP21c+Uyj0IIrqoNKNjxsfeVW6Wl i/TV6mmqxBxg49aXcM5Fb3OGw+kyKTBPXijaKBGZRRRTKCiiigAooooAKKKKACiiigCjFEGv7mIn ggMCOozVsbooyjBmXoGAyfyqG3xJfzyDooCZ9+9W3baPfsK1hsc1V+8VFEkoaIL8gOA7Dmro4FQC JlUsrkMeTnpUqOHQEVaIZHd263VtJC38Q4PoexrmrHwkjatDqWoyNJcxoUwGIDckAnHtjjpXV01l yQc496mS6lRl0MDWNAge1i+xqsUgkGd7MVYdwRmuevfD0mkyi8gkLyckhlxx3A9vau9AZ3y42qvY +tUNTia4iZBGGkHMZLYArCvRjON+p3YHG1KFRRvp1RxkaXDRyXmmsBHgtJGT909/wqxBdakjxebA XWUZHG3H404Gxs2klCSSK2UnjU7Qp749add3lxPdW9iI3FngFdh+Zl+tcVOrKKsnqe5Xw8asudxV ne72e3rq/wA0aknm28KGTYY3P3k/TNJTra3vGkuLCUCSEDbFMTyB/kU2e3uNPWP7U6MrHaHHHP0r 0oS01PnKkFGVk0/QKWOVradZ0/h4YeopoIPQg0taNXRmnZnRRyLIiyIcqQCDWmkgZFPJJHauX0q5 8uQ2zn5TzH/UVv2r7ZNp6GudqzNt1ctkB161WlhW5BWQHAPUe1WSik570BAPXFJq4JtO6KdtbRwA 7SGLEkA9qctqi3LTKBnGOKmwN3pzTZ5FgjaRzwo4xUWSRpzTm99WElukkRV/mU9z2rOk06O3VpYi zlRkA4p1veo8UsjeZ8vYnIq1ZS/aYcldntUPln6m8XVoX7dTA8uZzv2Mc85xXQabGYrRVf73Xkci pwyohLABR3rGTxXos10sUOoRNIeAo7k0QhGm7tlVq9TExtGOiN0kCkA5zQuCAc5zTq2OEKKKKYjK ooooLCiiigAooooAKKKKACiiigCnbOYpblNvO/OPrV1E53E5Y96qXIMLfalxhRh1PcUqXZ2+YkMp hIyG2n+VXGSS1MZwbehalUtGQOT6etEe3YCoAB9KSKZJkDocg0Q8x59TnHpWpg1YkooopgQzI0iB dw9CMVCi4X5Hw4PJapLmV0RvLAJ2n8axdR1C7tUikjtGmJOXwpwv1xXPOpGG510aE60kobskv7He rSW6Qeduy4YZ8326VVupLuGeIywJGGXbE4H3Cf4Tis//AITCfJVrSIDtjORXQ29yNSsVutqsno46 VipU6rbpvU76lHEYNRWIh7r03LCiO2sxM7DI53AYri/EOtjVnSONWWBOx7n1roNS+03umXMcSMhG PlP8X0rhCCDg9a58ZXlZQjsz0sgwNKUpVp6tPRFqxuJ1vIkSYjJ5DNxiusHSuJx371raRqAjd4ri UndjazGngsSl7kmVneWz/j0krLdJfibzA8FThlOVPoa37G6FxAko++OGHoRXOzXEVum6Vwo9z1qx pd2vniWE74pOHx2969CpY+bgpWvbQ7Jp0ji8x2AGKzpdYjyFjUnnknpUkxt5LUrLMuOAMHoT0qmm lI+UFyhkA6CuWbm/hO3Dxw6V6u5euL+GFP3ZDs38NZmtR3zaBM1u+bk/N6hRVWeIwTNGTkr3qzZX ssTiPIZGOMNWSq3laR1PB8lNTpO73Of8Hvq1xqUkd3Ms9mEJfGcBsjHX8a7O4lWytt6f7qCrMdvF AAkMaR7uTtUDNNngikCiVMqD0rfl5VZHA63tJqVTVEVoxubLdNg7gQQK5Sy8EtDrKSt5P2ZGLqQT vPseK7CFBDlY0IUVIB+9B2kcHvTS7kSlq3HZjmUYyBzT+tNZgAeeaVRhRVmQtFFFAGVRRRQWFFFF ABRRRQAUUUUAFFFFACModSrDIPUUKoRAi8KowB6ClooApTxi3nWZCVVyFcD17Gr8eNg2jA9Kq36l rGbHXYSPrT4izqNuVyverg7GVVXJjIBwOT0pNrnkseeopyoFHHX1p1aWvuY8yWxFEOWyOc808orD BANNYFG3Dp3FPBBGR0oirKwSbb5jKu9CsLiZZHgXeTnI7/WpTbgp5UcIQKNuB0q7KQFBPY1GJAwy Z1UntkVChGLdkazrVKkUpNuxXfeS0fIbpkdveuNvrQJffZrqLLu2EniHX3I712eCQ4UsXBPvxXPT 2c1jcXMrFp4plyAowQzHBx78mubF0+ZJ2PTynEOnOSvbTT19f8zAOlkz+VHdQMd2OG5H4U6/0Oaz jjdZ0n3nG2Mcj1NaVp4avYL8TLMkIiIkSR/Xr0rpb20N1aRCaVJArb18uMId3rkVy08K5RbtY9XF Zu4VIpT5o9dDz6K2muZgp3HuWboB61u6NPAGmgtvnjxtbnlj61n65HqqxvaC4ka1JyxY5JGOlYuk yjSdbtYwX8mbKkKmfmxx71FJctVKT3NsZJV8HKUF7q2sek2VyiH7PcKBuYFW9SOgNakEMdtJ5kSg Metc0zLJ+7VSzNwFx1rorWKSG2jjlcu4GCxr03FLY+WUmyaXTlvAZVbZIevcGskq0FxgjJVq6K0Y bCCec0otbfezlVJY5Oa550VJ3R20MbKmnGeqJIw7BWfAOOgp5GaAw6A0tbnDcbnsaYMM4+Y8VIcZ x3qLGG44z1qRokKDaQBSr90UFgO9NC7iScj6VQh9FRquRgs2R707Z/tN+dAjMooooLCiiigAoooo AKKKKACiiigAopy7c/N0pTsxwTQBDMN0Dj1U022I8mEjqVqxiM9zVK1Pl5hJ/wBScD/dPSqiyKiL 1FFFbnKBGRioMMpCMcL6jvUjyLGBnPJwAKY8sZGDnPp6VMu5Uew7yRuBLEgdjXnd94e1A6y8Ynuv nbKuudgH17V6NGSUGaViAvNROCmtTWlWnSk+VlWJcxCMEJgckYOaQANIE4LDH5DvTzlOoAQ9MjpT I3YM+1Ms3RqL62ZNtLolulR4SrAHcMYNYI064XUozZXcnI/1TZIUfjWpclraFpygLKueTkZrSs7V LeJeVJf5mb1NRVipepth6s6d0tmZE2h21+5827IuYz8pjAUfiP4vxqHRvC8+na413M1q8O3Cqqks D689PwrovKhgZnCDnkmpVYFQQQQ3RqxUE3d7nQ604xcYt8r3GTWcUhDrGgcdDioGt5VGduR9avKw ztJ5FOY/IeO1aGGxnxRO7g4IGc5NX9q/3R+VMR8KAx7VJTExCoPak2D1P506igQzyx2zn1zWdqFx PD5cSYDOetalZmrQtKIynVT19Kzq3Ubo6MMk6iUi5bhljVZMF8ckVPWT9s3WhRJB9oC8Fec1Lp1y Jo0jkk3ScnHeiM1eyHUoTSc2XsjzOD1FPqOTail8hdveqD6xErED5h6gVTko7mUKU6nwK42iiiqE FFFFABRRRQAUUUUAFFFFABRRRQAVTuR5NxHOCQrHa+O/pVyobmIzQMoOG6j60AWVOVBpGdUXcxwK qW85uI9o4YjDj+6e4qysIXHJYDgA84rdO6OSSsxB+8lVwPlUHn1qQqCMEUoGBgUVRJGDsOD07U8g MMUMoYYNRASqQARjPJ9Kjb0LXveo1zmIq3qOamKKw/wpn+rGPvE8AU23flkIKkHODRHVahLR6EV5 G7wrBuwkriMnGeDxWwkSqiheqjGTWXd8oij75ddv1z1rV5XAHU1nPRmtN3Qbep2446VTnza/vYhl D99R/OrvLZHQUxgq5B74/Ks5K+xtCXK9djKubgI6XcMuc8FDWhFdrNaiQ/IGOOTVK60+OV8xjY2f wNWzbRG2EMmPKXuTjmslzJs6pulKEV/w9iyWUp8pBFOLKvUgVRsYokkfy5S6+hOQKotcPcXZXymf aeMH+lU6jSWhnHDqUmk9EXdTvmtUQRnDMfTNUI9XuQo3FWJ746VPfwS3MayBSuwYIc1meWY3HmKQ uefesalSV7I7MLhqUo80tX2NQXWoSqdkYx61bkikubHZIDGx6j3qvaXcBnSCEMF9z3rTcZU1rGKa 3uctWcoSS5eU5020tldIXAK7sAjoattcWkLecq7ZVyoVf5mrl3Cs1mwx84+79aoCxS6jMgbbLkAq e1Q4ODtE6FWjWipVXa2jsV5NTuJY9hYAd8d6p1fl0uRZgkXzLjlj2ofSZ1bHB+lZShUb1OylWw0F 7rSuXKKKK7jwgooooAKKKKACiiigAooooAKKKKACiiigCpEfI1CXeMLMRsPuBzV+qU5JuoRxtGWq 6ORWtN6WOestbhRRRWhkFMlJWJiOoHFPqrc3ccTpGxwWJx+FTJpLUqEW5aCQOTKQxywO2reOc96r TxGWDEfDFeG9DToCyIEk6gAZ9TUxdtGVJcy5kS2iibUHc8rCuF9mPX9MVoN99azFcW18kpOI5Bsc 9s9q1SAwqJbmkPhEIIORTHYbgcZpx3rjByPpSkhgcdfSoLIXAYrg+uTimzRma3ZMBsnGKkzgcD5h QeoOBnNJpNFRbjJNGW2lSRljBKRjsaZDfTW8xSWEM46kDBq/d3Bt1LFM7jjg+1NuWjiC3EiYKgcj nrWTiov3Wdkaspq1SN77Fe51OK5tniRW3sOBWWk8icbsj0PNXLaGBpDMLjbtOcMv/wBeoZxbyTu3 nNyx6Jx/OsZty1Z20FCm3CK0/Udb3cUMwkMABH901tLewuVXd97p71lnTFaAGKQs/UZGAaW1tJfK fzE+5yoJ5zVRc4aGVaNCr7yeqNjA5YjIP6VTnmgtHaRzmR8fKPSso390rkGRs55Bpdq3gLkhHXlx 6j1FN1rqyWpEME4SvN6eRO2pTQTMWKsrj5c9Kmj1qFo1P3zjkqeKpuyfYsKvCthS38XrUdtcxWsA iS2j2jpSU+7KlQ5neMP0NOiiius8wKKKKACiiigAooooAKKKKACiiigAooooAqsC9/gZ4i7fUVaE b45fmq9vzfXB4wAo/SrlaQjpcwqSd7EeXXrgjvT1YMOKWmGME5BI+lXZoyunuOLAdTUMkYeYdiOc 5qTylx3z60LHhsk59KTTejRSaWqY8AAYHSmsu4e46U6iqsQnbUrzSxC3dZ/ukYI9fpVvTJLhrcpc QuhQ4VmI+YevFVrm2W5hKNweqsOqn1pqXd1aSo1xIJIW+UhV+6e2KymmbU2jZppQE5xz606ioNBo QAf41Gy7iW9O471IxyQuetNb5xtXIAPJxSaGnYyr2Yeb5TQlkyCCSatyRQTRqskgKnHG+pJ1IjkH X5TjgVjQ2E1wm9WABJ6msHdO29zuhyTgnflsJqMEUEyrF90jPXNRTIi20LAAMc5596fKZbVvKlCv xkZ5qaxWK8m2NAo4zkE1ja8mjuUuSmpN3S69zQtZEWxhMkgAx3qIM0d4GMo+YcMTww9/eo1ji8m5 UxLiLJHJqiskcjAeSxYdFUkg1o57HNCim5NfP5mjJYI940jjKsMgDuar3IgtrkSqVzj7i/1qYQXT WrSM21gdyqD29Ky59hcFTgt/CeoNTJ2VrGlJJyu56LQkuiZdsq/6s8BR/CfSq4x3qxbBiCNpaNjh gOopk1u8EpjIJx39azd9zrg4p8lzWooor0D58KKKKACiiigAooooAKKKKACiiigAooqOd/Lgd/QU AR2HPnN3MjD9auVR0kXC2S/aVUSEk5HfNXq3gvdRyVHeTCiiiqJCiiigAooooAKhuIPPjADsjK25 WXsamopNXBO2pLpk8s9pmYgujshIHXBxmrlZ+l5Fs5/6bP8A+hGr+4YrnOsCMj3piMQMFT7n3p+4 YznimqcuSAQMUAMkDsCR8oA5yOajSMIhVWAHXGKst9w/SoJ1eSEiPg461LXUafQyr2xnkdpQ3mHu AOlUjBcQr5hRkX1rX0+C6gGLmQu2OST1qpetLcXgtkLFMjGa56kEtep6WGxE5e47WX5Fi1tD9ib9 5nzhzx0p1tYrauzCQMcYxiq1+1xbCOJCyxhcZHc1esXJsUZuW/nVw5b2tsZVvaKDnfSQ+6bbasRy eMHtnNZlpHbajKJBFIroSNx7EHkfnz+Nazx+bE6OBtx3qmLYQxYju8Ec5/pVSWtzCnZx5b2M5/P0 +5YKce/rQdQuCSSw59quTxNM6QM5lyMiTHQ1myW8sTlGRsj0Fc0k46LY9alKFTWSVzXoooruPECi iigAooooAKKKKACiiigAooooAKinEDW8ouGKrgY4OD+VS0yWNZYmRhkEYpNXGnYmXGPlJK5+Un07 UtVdPdmtQrHJQ7c+uKtV0Rd0cUlaTCiiiqEFFFFABRRSZHrQAtB6UZHrSE+nJPQCgELpLM1o2R1l fP5mrxXAFUdIYG2bHeRyD2+9Wj1rktc7LtDSoI4pNpByOPYUudpwelKCCOKYDQCw5NKDt4PSk5V/ Y0Md3yj8aLCuNJbcxUAjpzUalJJT8o3r7f1pbqEyQlUYqex96ZbRGGNVLHP8Te9S9XY0SSjzXJDi RcOEIz0OKbJItvCZAAE9hUvlD+8euabJGBEQSSPSm12JUlfXYxJ7rfy8rkHoqAqKZA6SMYkhOZOO tbbQRO6MyKQOFHpSiKIsPlCuOAQKxdJt6s7Vi4RjaMSsHaO3W3jwkoGVBIqMw6kTkyJ+lOuIZf7S hkKnywMZFWwOvzMOe4quW+hk6igk1Z317lKiiitjnCiiigAooooAKKKKACiiigAooooAKKKKAKmn ttaeMnkSMcHryav1Rnhm+0pNb+Xuxtbf6U+G7PmeTOuyTt6N9K0hJJWZhUg2+ZFuiioJzIZII432 GR9pbGexP9K0btqYpXdieigWF10N0MeuwZpf7Ol73jk/7gqfaIv2bK7xJcX8EUqB0wWIP4VbbSbE j/j2jH4UQWBiuBM87SMqkAEAdfpVtu1ZSd3c2hGysUl06wx/x7J+VOGnWkbB0iVWHII7VbZeQQOn ahQDyKnUrQjghWLhc46/nU1N6P8AWnULQG7hTSpzxTqKYXGMQVIPWgIVHBOfelbjkU6kDInclgFI H1pyYKkHFAALHIBpxUHrQgYnlj1NMwdwK/dHan7D0DECnAYGKYEZVHB45qPAz8oyT2qdlBHv60Kd yg0ARqWAJI49PSpaay7hxwaVTke9AjLooooLCiiigAooooAKKKKACiiigAooooAKKKKACq18o+yt JjLRfOPw5xRRQBaicSRq3tUdx8rQyf3JAf6f1oorbeJy7TNiiiisTcKz9RiupHT7OxC4OceuOKKK TVxp2H6fHdpaKt24aTuRVnbtcEH60UUWHe4rsAyjPNPooo6iewhOBk0gcZAwRn1FFFMBSMikDYGD xRRSYIAcORTqKKEDCiiimIKZwjZxwaKKAAuOgILHoKcBgYoooGf/2Q==