20250216 12184000 1.0 TRUE <div style='text-align:Left;'><div><div>This dataset contains impervious landbase features updated using digital orthoimagery acquired in 2015 by the Delaware Valley Regional Regional Planning Commission (DVRPC) and its partners. This is an update of existing features originally captured in 2013 using 2010 orthoimagery. Additionally, estimated building heights were derived from high resolution normalized digital surface elevation data models generated from NIR LiDAR data using the highest hit method. Digital surface elevation models were derived from LiDAR data collected by Quantum Spatial and other vendors and compiled for delivery to USGS and its partners. The horizontal datum for this dataset is North American Datum, 1983, Geoid GRS 1980, and the data is projected in Lambert Conformal Conic StatePlane Pennsylvania South (FIPS 3702). Units are US Foot. The minimum size for building features is 200 square feet.Please note that since this data is an update to data originally created using 2010 aerial imagery, many of the features may not appear to be positionally accurate when used with newer imagery.Heights provided for 'Building' class impervious surface features are estimates of 90th and 50th percentile statistical distribution and should be considered approximate.Process Steps for Calculating Building Height Statistics:Normalized digital surface models (nDSM) and slope rasters were generated from 0.5-meter LiDAR data.Geodesic area was calculated (in square feet) for all features classified as ‘Building’.A negative 1 meter buffer was applied to all building features with an area greater than 200 square meters - applied in an effort to ensure nDSM input values were those corresponding to the building roof and not the adjacent ground.Zonal statistics (Spatial Analyst extension) were calculated on the buffered features for each nDSM raster (first multiplying each raster by 10 to maintain precision and then converting from floating point to integer raster) and slope raster (generated from each integerized nDSM raster). Zonal percentile statistics were also calculated on buffered features for each raster to obtain the 90th percentile building heights (HEIGHT_ESTIMATE field).All output statistics fields were joined to the original input feature class (with unit conversions applied, where necessary).A complete dataset, which includes the following fields that are populated with various statistical outputs generated during the building height estimation process, is available upon request:HEIGHT_PCT90 – 90th percentile value of all cells in the nDSM raster located within the building footprint.HEIGHT_PCT50 – median value of all cells in the nDSM raster located within the building footprint.HEIGHT_COUNT – total number of cells with tabulated values in the nDSM raster located within the building footprint.SLOPE_MEAN – average of all cells in the slope raster located within the building footprint.SLOPE_STD – standard deviation of all cells in the slope raster located within the building footprint.SLOPE_COUNT - total number of cells with tabulated values in the slope raster located within the building footprint.</div></div></div> DVRPC Pennsylvania Chester County Impervious Surface Structures Building Footprints Building Heights Buildings Roads Driveways Parking Lots Sidewalks Provide digitized impervious surface features using digital orthoimagery acquired in 2015 by the Delaware Valley Regional Regional Planning Commission (DVRPC) and its partners. Delaware Valley Regional Planning Commission (DVRPC) <div style='text-align:Left;'><div><div>https://catalog.dvrpc.org/dvrpc_data_license.html</div></div></div> 2015 Impervious Surface - Chester County, PA iVBORw0KGgoAAAANSUhEUgAAASwAAADICAYAAABS39xVAAAAAXNSR0IB2cksfwAAAAlwSFlzAAAB 2AAAAdgB+lymcgAAFEdJREFUeJztnetyGzcSRulYiaLYkVNO5eL3f6c8wv5INpXYVnRxLHvrG+0n tyAMOdTcgME5VSrKlERyKOG40Wg0Tj5//vx5BwBQASdrvwAAgKEgLACoBoQFANWAsACgGhAWAFQD wgKAakBYAFANCAuKR6WC+vjqq6+6f3/69Gn37Nmz7gPaAmFB0dze3nYfFhSiahuEBUXz4cOHLrp6 /vz52i8FCgBhQRUQWYFAWFA0X3/9dZezihGW5OU8FrQFwoKiOTk56eTkhLtgv367ICwoHkVSklSM qIiu2gRhQfE4okJYgLCgeOJ0UBBdtQvCAoBqQFgAUA0ICwCqAWEBQDUgLACoBoQFANWAsACgGhAW rI7bx7jeSh0a9G/tI0y34rgGS9t19KGtO3O8Bj9HWgMG64KwYFW85SaKQaI6VNWu759KJrE5YIQC 1fJAWLAqsRODPu/rfaUISEgsU4rk48ePvfJDWOWBsKAYbm5uOmmdnp52Uz1JSvLy9M8tZaJcxk4J PRWEOkBYsCqWhcTx77//dhGWc1cSk/u5x6mjJIZk2gRhwapYPBKSoqU0wX55eXl/n0Q19ZQQ+dUF woIiel1JRN9++22XU/KHBZb2wpqS+Bx+PWbO54WngbCgCJyX0u319XVX2iCZvH79urev+1RlB3rM eDKPQVjlgbDgERKB8kmKcjSA9bkS4YqAhGQivvnmm8mnhZKVPrwqaJl4NdEriPpe3T9VaUNuZZIa rPJAWNB7aKlX4Cww3SdJ6UOyuLq62p2dnY16ByU/nz0oQeox9eFoJ8pLUpE4nceaqmgU6oHfODwg JrglBAlFkZXul1AkLq3iWR5jcZRmYUlGLmvQrWVlkcZpIPml9kBY8IhY62RxCQnj3bt33ddfvnw5 2ZTwwR/kycn9c0qMllacIkK7IKzGcTGmRZAmnl3AqehH07P37993pQavXr3avXnzZnSex3ky4ame P9JyBr9WkuHtgrDgQd7K8nBpgaKcP//8s5OVIx5P26ZISsc6qEPH0RNdAcJqnFhNbhTJKPGtSOq7 777rRKHpn4Q1Zf7K23G82dkRlLfjKF+mCC9GXdA2CAseiUCykEguLi46Yf3444+d2HSfRBY3KI9d qZMIHanFqZ634OQq2/sKPWH7IKzGcYFmnN5JTLrfEc/vv//eCUJ5qx9++OF+xXAK4vOmU8Iow1RS iKpNEFbjpHvpHD25SDTWQMVygvhvgKVAWI0TK8djZKOCUOerXGmunJKgaBPWAmHBI1zl7khKotKH ShrcVeHFixddLdZSHOpACm2AsKAjnRb61nVSroNytbnuV55rjuLRPhAVICx4RCwiFSphcHlDFBo5 LFgahAWPSgqco3Kv8zUjm31V7VS8twfCgkfV7sL5K+PEe7w/HiABsAQICwZN72Ivqnjf1vNW6V7L lJjv8ybt+DMuD4FpQFhQNPsq2qeQWWwC6GLY2Afs0HPEaXTsIba2aLcKwoJVGbrFJnf681x1aH4O 72ncR4ymvIoq1s79bRWEBasyNIKZO8n+1DqvuHIqYSli8+v0pnGYDoQF1TCHrBwhuedXjK7SqOvQ a/LKahQWTAvCgmqZIuKyWOL0L+6vHCKtCLVp84KwoBjiKc8WhXu45w48nUpYvvWJ0/G+Q8Jy0j79 XqKreUBYUATxkImYvNb2H8nKzQPnWCVME+9+XJ9VuI9Y2jFEcDAOhFURHtDxkAjfL+aIQOYmnopz e/u522R9c3232fr29tPu/NVZ1/lUYvj++++7PJGFNkUdWHyMdDo35PHTqGro+20xezdB+phsf8qD sArHR1952pFLBuc2LteCpaCe8ZeX1931fv707P+HXtzsXn5/Wt01pVhA8XANryiqWaJPCUqjtdL/ s1kDhFU4GrjO36SbklPcXcHRRy3bZjRw//nnn93bt+87Mb/47i6S2u1ussd71TSQU1mlX3OUFaNn ZNUPwiqctABxX6tgbw+JewJL35zsSKObCt7cdI0D1WvrrqPp893Hj1+aBh6TDC+FnHw8pXdklU4L o+TgIQircFx4GKcRW6qk9sCNB07EAX19c3nfTDBS+7X7OnOHeFhWSOsxCKsSvPVD9EUXMce11ICO 9Upp5DfkNfjnLC2tCv7111/dz15dXe+en9x1hEgjxloGdCzTiO9J3DTdl4Ms/drWAGFVhHNZfQM1 /R97yQHtCPDY6M95G3U10Cqgku+aGvoxaz+i3r+vKC2/P/E061RktV7v3CCsipLuIneG376c1lLE COuY5/XCgDqanp6edcl3lTXcFXJ+s/vjv/+pJl/Vh5Pq4tDiSe3XOjcIq3D6Diodsmq25P/SY1Yl PUifP3+2Oz9/ufvw7YdOXBf//NWdg+gcVomDeciiguh7b9KIKv6HNOTxWwNhwaq4gFI4ktRCg27V S975qzSPNWXx6Nzsk04J/9nUBMKCVYm9pGLL5dxWnNzPblHcuep3uANhwap4ZXNfLVeOoQP60DRy bunte/y+3GPMTW5JylOAsKCoxHSsN9NtrEMTNVaB70uuxwR8mq+s7TqXAGHBqrgjQtxnFz/GTo3W HvSHIqx4jbme8kwNH4KwYFVy054hdVxbWUXzdUhW6krhEg9Jq8RV0bVBWFBc2UZsn7PVPM6XUo6H Zz9GalgBXRqE1RguB/CA6avzMkOFkW7Y1W1sjjeUoXsGp5LYkOtLu6Cm/cieQvrz+j2o5syPD3kQ VmPEqutjD1kwMSmu0gM/5lMa4K3NodXJvm4LfcT3dMmuGa2AsBrkUF+tQ3glz723tGFZuCLdiXTt D6xBWmafwOOiwL6fT6MxhDUtCGvjzJED8gkzHsAXFxf306QYmahSvSaGdIB46tdgGhDWxonHrY+p YUoHspvuCd2q6Z6fQ+LSfYfyYyUyVjo11onVRH1/UfBkpioF0BTw6uqqW4aXENUO5vXr193XJCsV e759+7aKFb5Dr7GGa2gJhLVxYg5lypU1n5TsiELJd0lMaCookSnKKv2o9pyQfH0u3Iw9rGBdENbG mWOQaarnD00FFWk5qtJA9/SwhoRz7v2RiO+OGbvt5BuPnz9EPFNRtzVOi0uGd7NCckvnGmTu1KlB 5vP7xkojt0VGU0Ldp1VADWxFV+/evetEpY6hf/zxR/f5MYM1DvQp6pxyJzP7eYQfv09Y6sclEevz n376aXd+fn7/8/te25jXTQR3GIRVOf4j9wks/jx+bezj585BjP2pNLA1wOP3xinVPuKG59yG4LHX cGgql75GR0Vq12wBa6tM7vslMyKoZUFYlRMHt/tHja2z6nsOowHrmis9pwauoi4339N9x5Y0DOmg OgVxg3Vf7so1ZMJNBGN0BuuBsDbEnDmjOMD1PK5w12BWBKJ/S2LuFjpUPH0FmVMuEMTHy8k3ysrC 8vXGwtd0alxTUexWQFgbLVWYuptBuk/QXTEVdUhUFpY5NhqZK6ry64grfX2RlaeDFlaa/0pza5Q8 LA/C2lBxqAeT80FzRVxx0FtcaR5qaPQRhRJZYmuL81Gxp3yfkFLR+XprWAndEgircmI73Tmjq9x9 ad4pnrt3DLnXeWjf3lB8sGyK8m6KoBQZ9tVZHTrphoT78iCsDTBnDugYgaWicsnDPvbJbapVznSP YCx8lXTSnlSOnHx/3xmQsDwIq0L6EsdjksG5TgO6dXL90OPnBvAhWaXPPfc+PDfI0/Vo9S+3kukp bvo6YtW7H8tRmnN4fa149HMxRyYsy315MHJkj0FYcE/M1yw5WKKo5ogUo4Tjc/RNRYfgaaRFZDGl JROWfZS8SkC8WEH3h+NAWNBxTJO6KUkT1z6fb6qz+eJqZZo0P7QwkUo7t1LqrTvHyF5bmGKtV3w9 fgymnHkQFjxg6YGSRlTuZiqmEFbfVDNW2MdaLOXddKuIKO4YiNPlGD31rW7Gf8fppCrn//777+5z dbhIVyX9fJAHYcGm2bdnUPe51kroc20z0u3Z2Vn3ESUSc3uxhMRfi6c3u0QifV49tvZ86n7JsbYm h2uDsOABS6425h7fIpiqvqnv9e9LdFs+IkZHuUgrrdOKgoz7PI3yVpbUWtPwmkFY0JEeTHFsAehT ScU0Ve4qpS9HFCXhbUY+7cfiSr8/Sj39+X3P772X2ljtAzzi4yCswyAsuCdX1rDEfrm5yxrS6Vos XYhJcn04+ok/s0+g6fadXB7Kj6PoSt+rqWa6Z7HvdSOxhyCshnq6D5kmHRvdaMXLdUhpLdLQrStz RxfexJyT776tOOnP5LYLpY+Zu440anz16tXe7x/ytVZBWBsnFUbMz/RNjY7BUcOYmqYS6MtNQVkg rIaZYsoR9+Kl1DLo02mwbxFXeSCsjZMWTqZV5fEgiacM0HT7Ta15l1w+qdZr2TIIa+NowOVauHhq qFog52v69tEdevz4mLWR5q18H5QJwto4llE6CBVZ6SPugfP3j3mu0jgkn76aKigThNUIuelOXGqP 7VT6fmaLeEr8lOgSlgdhbZzYCTQOSMnJxYv79todGsBpYWVtxK05ualhrde1VRDWBkkrsXP1R301 RsdOi0pvEXzoOvQe/Pbbb7tffvll9+bNm25zsu7zaUBpNTqsC8KC3jxPCwNUp/z8+uuv3XaZVMAt XH9tIKwNku53e8ohEC3x888/ZxvtcYxXeSCsjTJUQLlk/DE/Xzve6CxyG52hLBBW46RL+q1FXBcX F/d7+3yKjj8nwioPhFU5U9dPxW0qpSfUpyDtVQVlw29oIzx1CT4tSejbHL1V3P8qbRHTgqxrBGE1 Tq7+Krb93bqwJKZYyhB7V0F58FupnLFCSSMJDVqfl9cK8fSauTqewjQgLHjE1qMqqBeE1Th9SftW pNVaGUftIKzG6TtQoRVavOaaQVgAUA0Iq3Go6oaaQFiNs++IqX1f3yLxIApfP+UNZYGw4OChoy0Q T3x2/zCBsMoCYS28uTYeTpo7MzBliDDUt0m92XUrfE7gUwdbaRXues/0PrqQNValT/U6fc25GqxY /S/oTLoeCGtm3NnT4ppjQ60Hmh97ywNqDZn2NUGE5UFYMyNZ6XRkRUAvX77s9q4JCWyKLSCOOjSg PJAtrCFba9KuBLl2yqVQ2usZerI1TAfCmpmrq6vd5eVlJ4azs7MHU4womTHEQxTifUPw68jtJSxJ EHEKuORxXDGnlR7UUdL70woIa4EWvI6qfNu98Scnk/7B546k3xIxelwSn92o/3D0GvT7dM8shLU8 CGtm9Md9fn7+IKHrP/6p8iK5gTO0TXIqgDSpXXpniblfp99HHwem39nU/9nAcBDWAqQ5ojj1Ghsx 7Bs4QwZVKs1SczJrTVEtKE+To6xaaL9TGghrZqKUHFk50R6XyqFc3G5Hv8e4SKLfJ3Vay4KwZiZG LLVEMzVMBec4+TlGTk60+2Ts09PTRz+LrJYHYUF1TNkN1aKKj2WBbW3hYgsgLGi6X1dfbozcVJkg LCgaR1IkuEEgLGievsUPoqzyQFhQNEv160rzVciqTBAWNN2vKz2T0feVtjUJ7kBYUB0Wy1R5rVxC H1mVCcJqgNhFM9Yauc6oZGIzvVgv5U3JU9WyIak6KPuvFUaTDvbc10qmT0gceNomCGvjeA9cbuCv Ne2JleTHbrj2ih67BNoEYW0cD2wLItdmeE5yeabYP92vse/73GBQX9Pn2kqj+9XmBWm1B8LaOJaA B7s3YLvne+nJ5XggRpQc3T7bBGE1gqvFYy+upfpJ5e6L09R922P0Ov19upVo40Ee0BYIqxE0wDWN srjWTFof07wwvkYS7YCwNk48ZMLRSnrwROn0FYkyLWwPhLVxUjHFBoJTsGSb4hTJVwd8aJroPuti yCEarDbWCcKCUSyVtO97nvTkoaFbdob2vIeyQFgwOXFVb2yeLI3gYoW7WxbP1SsLygNhwWyymiKC yQlLOTiRnhOYi672TVkRW30gLNg8OWkxJawThAWTMnV30PRxYk1WJI2uXNm/L/lOhFUfCAuKbrqX E1YuL9Y3dYyrh1A/CAtGsVSv9X3P05e7srDoB78dEFZhxA3KNUxZnlJSMGYvoR8/PZYrfc60Ngu2 AcJamVyRYy2yWgLeC4ggrJX58OFDtzTveiJE9ZC+6ZwjL1rMtAXCKgREtf8o+bSiXVCp3h4Ia+1f wMlJVRuR1xCWo6hUWki+PRDWyuQiB/hCX9U60+c2QVgFwjL8F9TDSyB1EAhrZdKeTnOUBtTMkCJR aAeENVGOxQMrFc4hAeX6VdVK32resT/zlOeBNkBYlR+1Xjpbvz5YFoQ1knRv25AuAPEkZlYIAYaD sEby1NogaogAjgdhzSQspkIA04OwJsTte4fUCdFADuB4ENZExOPXJSPd9uWnpuwXBdASCGsk6THq fT3NWz0oYY6yBmgXhDWSGC2ltVh0E0BIMC0Iq4K+5gBwB8IaSd+qYCuyiosM8RZgDhDWSBxJtRpR eZEh7aQAMAcICwCqAWGNpPWowgsNrb8PsAwIayStb26mpzosCcKagVjWsHVhASwJwpqBmqZHbiAo uV5fX3fV+e7yqRN9/DlACSCsgae2aFDXIKCnTul0nT5yTAeQ6lp1QAZASfAXOXDZXmxVWr42CSpe 41avFeoFYR3AA3irp7Tc3t52UZU+Xrx48ehrRFlQEgjr0Bu0Z1q0haT6vs3JtV8bbA+E9UTcjaH2 QW0h61oUUXnDtqMugJJAWCOoXVa5SMstcracr4N6QVgHUNSRto/xv7dyfT5Iw4l3R1pbuUbYDgjr AGtPi+aumM9d39rXDNAHwiqc1rf+AEQQ1kBiy+O1Sxw41xBaBWENxD3bRQkJac41hBZBWABQDQhr IHGVcO3oyq+BKAtaA2ENZO28VYS9ftAqCKtwWj/kAiCCsAoHMQF8AWEBQDUgLACoBoQFANWAsACg GhAWAFQDwgKAakBYALCrhf8B3J/qESwJP6kAAAAASUVORK5CYII=