Identify observations that do not fall within a proposed lot

Reference no: EM133661135

ArcGIS Spatial Join

We are ready to start asking questions about our proposed lots. It would be helpful to have the information from the observations collected by the field team associated with each parking lot. A spatial join will be used to add their observations to the proposed lot polygons, but first we will adjust the observations so they all fall within a proposed lot. In addition, your market research team has determined that many EV owners wish to charge their car during the work day. You obtain some census data at the zip code level that includes the number of employees. This information will be used to identify lots located in zip codes with more than 5,000 employees per square kilometer.

• Download and unzip the Stage5Data folder. Using Associated Spatial Data - Observation information is added to the proposed lots.

• Identify the observations that do not fall within a proposed lot. Move these points so they fall within the boundary of a proposed lot.

• Use a spatial join to add the site observation attributes to the proposed lots. Save the result as ProposedLotsWithObs in your Analysis feature dataset.

• Add the ProposedLotsWithObs feature class to a map (if not already added). Change the layer name to Public 24 Hours and use a definition query to display only those lots open 24 HOURS with Public access. Note the number remaining to include in your reflection. Using Associated Tabular Data - Business statistics are associated with zip code boundaries and new information is derived.

• Use a table relationship to associate the data in the BusinessStatistics.xlsx spreadsheet with the Hennepin County zip code shapefile.

• Select zip codes intersecting the Minneapolis boundary and export them to your Analysis feature class as ZipCodeBusinessStats.

• Add a new field to ZipCodeBusinessStats named EmpPerSqKm and calculate the values as the number of paid employees (EMP) per square kilometers of land area. Use the ALAND10 field, which is the land area in square meters and account for the conversion to square kilometers in your calculation.

• Find proposed lots that are located in zip codes where there are more than 5,000 employees per square kilometer. Reflection

• Why do you think we moved the observations so they fell within a proposed lot?

• How many proposed lots remained in your Public 24 Hours layer?

• What type of table relationship did you use for the business data and zip code boundaries? Why?

• How many proposed lots are located in zip codes with more than 5,000 employees per square kilometer?

Reference no: EM133661135

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