<!DOCTYPE article
  PUBLIC "-//NLM//DTD Journal Publishing DTD v2.0 20040830//EN" "http://dtd.nlm.nih.gov/publishing/2.0/journalpublishing.dtd">
<article xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" article-type="research-article" dtd-version="2.0" xml:lang="EN">
  <front>    <journal-meta>
      <journal-title>Journal of Geography and Regional Planning</journal-title>
      <issn pub-type="epub">2070-1845</issn>      <publisher>
        <publisher-name>Academic Journals</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.5897/JGRP2023.0864</article-id>
      <title-group>
        <article-title><![CDATA[Sentinel-2 visible and near-infrared reflectance signature data for mapping potential geolocations of curb cuts in Hillsborough County, Florida]]></article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author" xlink:type="simple">
        		        	<name name-style="western">
	            <surname>Heather</surname>
            <given-names>McDonald</given-names>
	          </name>	
        		        	<name name-style="western">
	            <surname>Namit</surname>
            <given-names>Choudhari</given-names>
	          </name>	
        		        	<name name-style="western">
	            <surname>Kayleigh</surname>
            <given-names>Murray</given-names>
	          </name>	
        		        	<name name-style="western">
	            <surname>Leomar</surname>
            <given-names>White</given-names>
	          </name>	
        		        	<name name-style="western">
	            <surname>Brooke</surname>
            <given-names>Yost</given-names>
	          </name>	
        		        	<name name-style="western">
	            <surname>Joseph</surname>
            <given-names>Bohn</given-names>
	          </name>	
        		        	<name name-style="western">
	            <surname>Benjamin</surname>
            <given-names>Jacob</given-names>
	          </name>	
        	        </contrib>
      </contrib-group>
      <author-notes>
		<corresp id="cor1">* E-mail: <email xlink:type="simple">hlmcdonald@usf.edu</email></corresp>
      </author-notes>
      <pub-date pub-type="collection">
        <year>2024</year>
      </pub-date>
      <pub-date pub-type="epub">
      	<day>30</day>
        <month>04</month>
        <year>2024</year>
      </pub-date>
      <history>
      			<date date-type="received">
			<day>23</day>
			<month>10</month>
			<year>2023</year>
		</date>
						<date date-type="accepted">
			<day>26</day>
			<month>04</month>
			<year>2024</year>
		</date>
			  </history>
      <volume>17</volume>
      <issue>2</issue>
	  	  <fpage>25</fpage>
	  <lpage>33</lpage>
      <permissions>
		<license xlink:type="simple">
			<license-p>
			This is an open-access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
			</license-p>
		</license>
	  </permissions>
	  <self-uri xlink:href="http://politicalwaffle.uk/journal/JGRP/article-abstract/D29B58772155">
		This article is available from http://politicalwaffle.uk/journal/JGRP/article-abstract/D29B58772155	  </self-uri>
	  <self-uri xlink:href="http://politicalwaffle.uk/journal/JGRP/article-full-text-pdf/D29B58772155">
		The full text article is available as a PDF file from http://politicalwaffle.uk/journal/JGRP/article-full-text-pdf/D29B58772155	  </self-uri>
	  
      <abstract><![CDATA[A curb cut is a ramp that connects the sidewalk to a street crossing, thereby making it accessible for physically disabled pedestrians. Initially, ArcGIS Pro and machine learning algorithms in Python was utilized to classify a dataset of curb cut spectral signatures, leveraging Sentinel-2 imagery with a 10-m resolution. Initially, multispectral visible and near-infrared (NIR) Sentinel-2 sensors, along with machine learning geoprocessing tools in ArcGIS Pro, were utilized to generate signatures from curb cuts manually identified using Google Earth in Hillsborough County, Florida. Subsequently, we interpolated these capture points using a Python-modified Bayesian Maximum Likelihood Estimator to produce a cross-county signature map. The resulting layer was overlaid onto a zip-code gridded land use land cover (LULC) map and analyzed using a semi-parametric eigendecomposition eigen-spatial filtering approach. The hot/cold spot residuals represented independent curb cut clustering propensities. Utilizing higher sub-resolution satellite signals can optimize the identification of LULC classifiable, zip-code gridded capture point signatures, thereby improving the predictive mapping of other curb cut geolocations, such as those in school parking lots and homeless shelters. A real-time satellite mapping system can utilize sub-meter resolution data in a mobile application to retrieve a ranked list of visually similar curb cut geolocations.

	 

	Key words: Curb cuts, curb ramps, satellite data, ArcGIS, python, eigenvectors, Hillsborough County.]]></abstract>
    </article-meta>
  </front>
      <body/>
    <back>
		<ref-list>
			<title>References</title>
						<ref id="ref1">
				<label>1</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Architectural and Transportation Barriers Compliance Board (2023). Accessibility Guidelines for Pedestrian Facilities in the Public Right-of-Way: A Rule by the Architectural and Transportation Barriers Compliance Board. Available at:
				
					View]]>
				</mixed-citation>
			</ref>
						<ref id="ref2">
				<label>2</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Adams MA, Phillips CB, Patel A, Middel A (2022). Training computers to see the built environment related to physical activity: Detection of microscale walkability features using computer vision. International Journal of Environmental Research and Public Health 19(8):4548.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref3">
				<label>3</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Ai CB, Tsai YC (2016). Automated sidewalk assessment method for Americans with Disabilities Act compliance using three-dimensional mobile lidar. Transportation Research Record 2542:25-32.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref4">
				<label>4</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Al-Ahmadi FS, Hames AS (2009). Comparison of four classification methods to extract land use and land cover from raw satellite images for some remote arid areas, Kingdom of Saudi Arabia. Journal of King Abdulaziz University, Earth Sciences 20(1):167-191.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref5">
				<label>5</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Brown SE (1999). The curb ramps of Kalamazoo: discovering our unrecorded history. Disability Studies Quarterly 19(3):203-205.]]>
				</mixed-citation>
			</ref>
						<ref id="ref6">
				<label>6</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Brumbaugh S (2018). Travel patterns of American adults with disabilities. Bureau Of Transportation Statistics, Washington DC, WA, USA, US Department of Transportation. Available at:
				
					View]]>
				</mixed-citation>
			</ref>
						<ref id="ref7">
				<label>7</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Chun Y (2008). Modeling network autocorrelation within migration flows by eigenvector spatial filtering. Journal of Geographical Systems 10(4):317-344.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref8">
				<label>8</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Eisenberg Y, Heider A, Gould R, Jones R (2020). Are communities in the United States planning for pedestrians with disabilities? Findings from a systematic evaluation of local government barrier removal plans. Cities 102:102720.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref9">
				<label>9</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Griffith DA (2003). Spatial autocorrelation and spatial filtering: Gaining understanding through theory and spatial vizualization. Springer.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref10">
				<label>10</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Griffith DA, Chun Y, Li B (2019). Spatial regression analysis using eigenvector spatial filtering. Academic Press.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref11">
				<label>11</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Hara K, Sun J, Moore R, Jacobs D, Froehlich J (2014). Tohme: detecting curb ramps in Google Street View using crowdsourcing, computer vision, and machine learning Proceedings of the 27th annual ACM symposium on User interface software and technology, Honolulu, Hawaii, USA.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref12">
				<label>12</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Hillsborough County (2022). Hillsborough approves an additional $20 million in sidewalk repair funding. Hillsborough County. Available at:
				
					View]]>
				</mixed-citation>
			</ref>
						<ref id="ref13">
				<label>13</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Hu Z, Hu H, Huang Y (2018). Association between nighttime artificial light pollution and sea turtle nest density along Florida coast: A geospatial study using VIIRS remote sensing data. Environmental Pollution 239:30-42.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref14">
				<label>14</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Jacob BG, Izureta R, Bell J, Parikh J, Loum D, Casanova J, Gates T, Murray K, White L, Aceng JR (2023). Approximating Non-Asymptoticalness, Skew Heteroscedascity and Geo-spatiotemporal Multicollinearity in Posterior Probabilities in Bayesian Eigenvector Eigen-Geospace for Optimizing Hierarchical Diffusion-Oriented COVID-19 Random Effect Specifications Geo-sampled in Uganda. American Journal of Math and Statistics 13(1):1-43.]]>
				</mixed-citation>
			</ref>
						<ref id="ref15">
				<label>15</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Jacob BG, Novak RJ (2014). Integrating a Trimble Recon X 400 MHz Intel PXA255 Xscale CPU mobile field data collection system using differentially corrected global positioning system technology and a real-time bidirectional platform within an ArcGIS cyberenvironment for implementing mosquito control. Advances in Remote Sensing, 3(3):141-196.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref16">
				<label>16</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Kraemer JD, Benton CS (2015). Disparities in road crash mortality among pedestrians using wheelchairs in the USA: Results of a capture-recapture analysis. BMJ Open 5(11):e008396.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref17">
				<label>17</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Maselli F, Conese C, Petkov L, Resti R (1992). Inclusion of prior probabilities derived from a nonparametric process into the maximum likelihood classifier. Photogrammetric Engineering and Remote Sensing 58(2):201-207.]]>
				</mixed-citation>
			</ref>
						<ref id="ref18">
				<label>18</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Meldon P (2019). Disability history: The disability rights movement. National Parks Service. Available at:
				
					View]]>
				</mixed-citation>
			</ref>
						<ref id="ref19">
				<label>19</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Meyers AR, Anderson JJ, Miller DR, Shipp K, Hoenig H (2002). Barriers, facilitators, and access for wheelchair users: Substantive and methodologic lessons from a pilot study of environmental effects. Social Science and Medicine 55(8):1435-1446.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref20">
				<label>20</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Mingguo Z, Qianguo C, Mingzhou Q (2009). The effect of prior probabilities in the maximum likelihood classification on individual classes: A theoretical reasoning and empirical testing. Photogrammetric Engineering and Remote Sensing 75(9):1109-1117.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref21">
				<label>21</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Murakami D, Yoshida T, Seya H, Griffith DA, Yamagata Y (2017). A Moran coefficient-based mixed effects approach to investigate spatially varying relationships. Spatial Statistics 19:68-89.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref22">
				<label>22</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Park YM, Kim Y (2014). A spatially filtered multilevel model to account for spatial dependency: application to self-rated health status in South Korea. International Journal of Health Geographics 13:6.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref23">
				<label>23</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Smart Growth America (2022). Dangerous by Design 2022. Smart Growth America. Available at: View]]>
				</mixed-citation>
			</ref>
						<ref id="ref24">
				<label>24</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[Stahler AH (1980). The use of prior probabilities in maximum likelihood classification of remotely sensed data. Remote Sensing of Environment 10(2):135-163.
					]]>
				</mixed-citation>
			</ref>
						<ref id="ref25">
				<label>25</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[State of Idaho (2023). ADA curb ramp program. Idaho Transportation Department. Available at: View]]>
				</mixed-citation>
			</ref>
						<ref id="ref26">
				<label>26</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[U.S. Census Bureau (2021). Disability characteristics. American Community Survey, ACS 5-Year Estimates Subject Tables, Table S1810. Available at:
				
					View]]>
				</mixed-citation>
			</ref>
						<ref id="ref27">
				<label>27</label>
				<mixed-citation publication-type="other" xlink:type="simple">
				<![CDATA[U.S. Department of Justice (2020). The ADA and city governments: Common problems. ADA.gov. Available at:
				
					View]]>
				</mixed-citation>
			</ref>
					</ref-list>
	</back>
    </article>