20170825 08415500 1.0 TRUE Walk to Food Distance Analysis using 1640 ft distance along streets in Riverside. Josphat Mutunga Master's Thesis results for University of Redlands Master Program. Distance to Food used the following layers for Network analysis towards Stores with retail food supply. Criteria included Areas that had at least 33% of the population living within 500 meters (0.3 miles) from food store. The data was used to subtract these areas away from the Block groups that met USDA Food Criteria. Food Desert areas are identified to help the City of Riverside identify areas for potential improvement of food availability for disadvantaged communities. Layers included: Food Desert Block Group Defined by USDA Criteria. This data was compiled as part of Josphat Mutunga's Master Thesis for the University of Redlands program. 2017 and clipped Distance analysis of .3 miles from food sources ( see criteria below) from Block groups that met USDA criteria for "Food Deserts". Food Desert Criteria: Growriverside Food Sustainability Distance Analysis Walk to Food Food Deserts Distance to Food used the following layers for Network analysis towards Stores with retail food supply. 1640 ft (0.3 miles) from food store City of Riverside, Josphat Mutunga MS Thesis Results 2017 MSGIS University of Redlands Please Cite City of Riverside GrowRiverside /9j/4AAQSkZJRgABAQEAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCACFAMgDASIA AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK U1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3 uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwD3+iii gAooooAKKKKACiiigAooooAKKKKACiiigAooooAa8iRRs8jBUUZLMcACiORJolkjYMjgFSO4rL8R WU99pZjtxl1YOVzywGeBUmhWc1jpUcU/Dkltufu57Vt7OPsue+t9jPnl7Tltp3NKiiisTQKKKKAC iiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKQkKpZiABySe1AC0VhPrRn1uzt7KZJLd wfMwM+vftW6enFaTpyhbm6kQmp3t0Cis7R5LqS1l+1qRKs7rk9CM9vbt+FaNTOPLJxHGXMrhRRRU lBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABXCatq+pve3FqZyiIzDbEMcD369 K7uuXvPChub6e4FykMbtuChM49e4967MFOlCTdQ5cVGpKKUDntMu5rC6W5iVto+V2C7uOp/QV31h qEWoQ+ZGrp32SYDYPQ4B6GuT8Qh7Ce1htnZIxAvzJwHI4ycdTgCooBfJrJvLeQMG/evIWyoQ8/N3 Ax2PpxXbXpRxEFU2OalUlRlybnU+IJZ4dGne3Yq4xlgcEDPNQ2uoTQeH7a5aKS6fGG2MGOOeSR9K zZf7XudClZlgvI3csrAEtt56DA79O9a2hx3cVmqT28MMRXcipkEE9QQa5JQjClZ2dn/XmdCk51Lq 6uh+kaumrxyOsLx+WQDk5Bz6GtKqV2bxf3FnbptZceaZNuzOe1U/Djai1k324NgN+7aQnefrntWE 6alF1I6Ltc2jNpqEtX3sbNFFFYGoUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUVQ1TV7fSolabLO 5wqL1PqaqMJTfLFailJRV2X6KgtLyC+gE1vIHQ+nY+hqSYMYJAqB2KnCk4B9s0nFp2YXTV0QwahZ 3M7QwXEcjqNxCHIx9elVNc0p9VtFjjmMbocgE/K31rl47S70fWLMsI/OZuFUEhgeD2/Diu8rqqw+ rzjOm7nPTl7aLjNGPDoMMul29rqAWWSEEK6MRjJ7fp1qlpnh65sNYeUT7bZemDkyA/wkV0tMjljm XdFIrrnGVORmoWJq2kr6Mt0Kd0+qCKKOCMRxIEQdFHQU4sFGSQOcc0tcz4vt7mSK3liWR4kJ8wKe AeMHH581FGHtaii3a5VSfs4OSV7HTUVW052fTrdnWRWMYyJfvZx3qzWclZtFp3VwooopDCiiigAo oooAKKKKACkYlUZgCSBnA70tY+sa9HpMsUXl+a7fMwzjC/41dOnKpLlirsmc4wV5MzovEepTTJCm nbpEYrMoVjjnH4d+tS6NqGoz6vLHfb1yuPKKhQp6jGTnp6Z61n6ZPHL4sZvsufMdipdiWQ4Jzz/W pho94NZjeSziYNcM7S9V2cdQDgd/xr05wpRvFpK6v/WpwQnUdpXb1/robGparcWd9BbW9nJPu+Z9 vPy9OP8AE1pSSMkDSLGzsFyEGMn2rD1WHHiDT5EtA7uciRW2n5eueoxjB9a3yQASTgDrXBUUVGDS OuDk5SuzG0jWbm/vZbe4smg2ruBweOehzTfEcoaBLY6fLcszAqwHyg59Rzmn2HiGPUdR+y29u5QA sZGOMAd8fXH51s1c37KqpONvK5MV7Sm0pX87HM+HdYXzF0uSBYnXITy84JGSQfeulJCqWYgAcknt UBsrU3S3RhQTrnEgGDzx+NUtQvZZ7Z4dL8m4mYFW/eL8o6HgnrUz5a1S8Fa+44XpwtJ37Es+p2wj kkgX7XLCMlIsFlpul3t9d7xeWJt9vRs8H2weazbCS18PyQ2UkchubsKxKgHaTxt69Ac10lOqowVk rp7MKblN3b23QVj6Ro02lXU+LkSWzjIQjnPrWxRWMakoxcVszWUE2m+gUUUVBQUUUUAFFFZ+salD ptmWkLbpMqgTrn1H0qoRc5KK3JlJRV2aFFZOn2uox3MUst951u0I3Kw5Le1a1OcVF2TuEZOSu1YK KKKgoKKKKACqV5pFjfyrLcQKzr3BwT9cdasSXVvFJ5ck8SPjO1nAOPXFZ+q6yNNaNRAZfNRjGynq wxgY/GtaUajkuTczqShy+/sUde1X+z71Ps0MJuEUOzsuTtPGPX071uWUzXFhbztjdJErnHTJGa5r Q2Gqa3c3V5AfNKDClMpjp378D9a07zW4NM1GDT1txsIUEq20R5PpiumrS2pRXvJamFOpvUk9GS63 qjaVFFKIlkDEjk8g4OMfj19qda6vDLYwz3nl2pmzsV3+8PXtUt3cWMgkt5PIuJUBYW7EFmIBwAD3 rk7fSdS1W88q6W4ghTcV8xTtQei5/Dp6UUqVOdP39Ldf8kFSpOM/d1v0NEamya4trplhbqjOFklV M714+bjGAOa6iorWBbW1igTlY0Cg+uKqalqX2UCC3AlvXGY4cdQOufwBrGbVWSjBf13ZrFOnFuT/ AK7FLxZM8WkAI21ZJAjY6kYJ/pXP2iLosun6m5aaOUMdqDG3jB69etdrdWcF/biK6j3pkNt3Ec/U UwaZZ/ZY7ZrdJIo8hA43Fc+hrajiYwp+za739DKpQlOpzp+hFdabZ6skM80TBsBlYcMB1wa0KAMD A6UVxyk2rPZHSopO4UUUVJQUUUUAFFZKeIrB9R+xBm3btok42E/XP4VrVc6coW5lYmM4y+FhUU9v Dcx7J4kkXOcMM1Iyq6MjDKsMEeopkEEdtCsUK7UUYAqU7arcb106ElFFFIYUUUUAFFFFAGNf+Hbf UNRF3JK69N6ADDY6VY1LSo7/AGSq7RXMWPKkHIU5z0rRorX29TTXbYz9lDXTcigEkdsguJFeQL87 gYBNZbNpWuXz27xM81sSNx46HsQeRV3VZbeHTJ3ukZ4MYdVzkgnFZ1imnK1jcWNncAsCAyocBSTn e3T175rSmvdc9b9LbEVHqoaW8yjoVlp0erb4rqRriLePKdOR2JyOP/111dUbXSLKyumuYIikjAg/ MSOee9XqnEVVUnzJv5jo03CNmjnbbxdayRqbiGSJs4YgblH9aqRW93c+JI9RghdbWZwRJtH3cYPu M4P5+9aWuWemQ2DTz2owrDiI7CSeO3B/Gruk3dveWCG1SRYYwI1D9eAPeujnhCDqUo76O+xjyylJ QqS21L1FFFcB2BRRRQAUUUUAFFFFAFJdJsEvPta2yLPnO4Z6+uOlXaKKqUpS3dxKKWyCiiipGFFF FABRRRQAUUUUAFFFFAEdxbxXUDQzLujfhlyRn8qggsfs128kUrLAUCiAfdU+oHb/APXVuiqU2lbo S4pu4UUUVJQ1445QBIivtO4bhnB9ar2ln9jaUJITE7FhHtACE+lWqKpSaVhcqvcKKKKkYUUUUAFF YGqahrMGppHZ2TPAB12Fg+fUjpit2Mu0amRQrkAsoOcH0zWk6TjFSfUiNRSbS6DqKKKzLMnTtRlu tX1C3chUhICIRz6E5/z1rWpojQSFwihyMFsckU6rnJSd0rEwTSs3cKKKKgoKKKKACiiigAooooAK KKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA//9k=