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这项作业的目的是为了更好地理解可及性的重要性。学生们将计算出每个普查区的基于重力模型的就业可及性指数,以及香槟-乌尔巴纳大都会区的整体区域可及性(根据1999年的人口普查定义)。学生还将比较估计的就业可及性指数值和该地区的区级平均单程通勤时间,以研究这两个变量之间的关系(想想哪个是因变量!)。

这是一项个人作业。本科生可以在两个学生的团队中进行分析,但每个人都应该写出并提交一份个人报告。如果你和另一个学生一起工作,请在报告中报告你和谁合作了!

I.在Compass课程页面上传的文件。
1.Assignment 2_CU.xls有三个工作表。
-Jobs_workers包括工人和就业人数,以及各普查区的平均通勤时间。
-Tracts_OD包括原籍(O)和目的地(D)两个普查区之间的单程平均通勤时间。原来的表格包含一些O-D对的零,这表明在这对人口普查区之间没有通勤者。现在,零被10,000所取代,这是一个随机的大数字,假设这些O-D对之间的通勤存在重大障碍。这个大的通勤时间在计算相应的出发地的工作可及性时,会在目的地区的工作机会上打折扣。
-可及性_3区的例子介绍了一个三区案例的可及性估计,我们在课堂上使用过这个案例。虽然这个三区案例的表格比C-U区的小,但表格的结构是完全一样的。因此,你可以在这个工作表上找到计算香槟-乌尔巴纳地区的人口普查区的可及性指数所需的公式和提示。
-某些版本的ArcMap在导入”.xlsx “文件时有问题。这就是为什么我把excel文件保存为旧的”.xls “格式。继续使用旧的格式。

2.GIS文件(cu_tract_utm16.shp, dbf, shx)也已上传,以便你能将你的结果可视化。

二、任务。
1.估计香槟县所有普查区的就业可及性指数(重力测量)。用估计的区级可及性指数制作GIS地图。讨论估计的可及性指数的空间模式(例如,哪里的分数高,哪里的分数低)。
2.估计整个地区的就业可及性。
3.检查估计的工作可及性和人口普查区层面的平均通勤时间之间的关系。这两个变量是否有明显的关联(相关)?这种关联是正还是负?你可以使用任何你熟悉的方法或工具来解决这些问题(例如,散点图、相关分析、回归分析或绘图)。
4.讨论一下以下问题。一项旨在改善城市地区就业与住房平衡的政策是否会对该地区的平均通勤时间产生重大影响?你认为就业可及性对区域土地使用和交通系统来说是一个很好的绩效指标吗?一个区域规划机构可能希望定期(如每半年)估计区域水平的可及性指数,以监测区域水平的可及性变化。您认为您所估算的无障碍措施是否能达到这个目的?您会考虑对无障碍措施进行何种修改?为了回答这个问题,你可能要考虑本地区的就业增长会如何影响这个无障碍措施。

三、可交付的成果。
1.在Canvas作业页上提交一篇3至5页的MS-Word文件,介绍你的分析结果和对上述问题的回答。
2.同时提交你的MS-Excel文件到Canvas作业页面。

The purpose of this assignment is to better understand the importance of accessibility. Students will compute a Gravity-model-based job accessibility index for each census tract and overall regional accessibility in the Champaign-Urbana Metropolitan area (by 1999 Census definition). Students will also compare the estimated job accessibility index values and tract-level average one-way commute times in the region to investigate the relationship between the two variables (Think about which is the dependent variable!).

  • This is an individual assignment. Undergraduate students can work on analysis in a team of two students, but each should write up and submit an individual report. In case you work with another student, report in your report with whom you have collaborated!
  1. Files uploaded on the Compass course page:
  2. Assignment 2_CU.xls has three worksheets:
  3. Jobs_workers includes the numbers of workers and employment, and average commute time by census tract
  4. Tracts_OD includes one way average commute time between pairs of origin (O) and destination (D) census tracts. The original table contained zeros for some O-D pairs, which indicates no commuter between those pairs of tracts. Now, zeros are replaced by 10,000, a random large number assuming that there are significant barriers on commuting between those O-D pairs. This large commute time will discount job opportunities at the destination tract to an extreme extent in calculating job accessibility of the corresponding origin tract.
  5. Accessibility_3 zone example presents an example of accessibility estimation for a three-zone case which we used in class. While the tables of this three-zone case are smaller than those of the C-U region, the structure of the tables is exactly the same. Thus, you can find formula and tips on this worksheet that you need to compute accessibility indices for census tracts in the Champaign-Urbana region.
  6. Some versions of ArcMap has a problem in importing “.xlsx” file. This is why I saved the excel file in an old “.xls” format. Keep using the old format.
  • GIS files (cu_tract_utm16.shp, dbf, shx) are also uploaded so that you can visualize your results.
  1. Tasks:
  2. Estimate job accessibility index (gravity measure) for all census tracts in Champaign County. Make a GIS map with estimated tract level accessibility index. Discuss about the spatial patterns of estimated accessibility index (e.g. where is the score high and low?).
  3. Estimate overall regional job accessibility.
  4. Examine the relationship between the estimated job accessibility and average commute time at the census tract level. Are these two variables significantly associated (correlated)? Is the association positive or negative? You can use any method or tool you are familiar with to address these questions (e.g. scatter plot, correlation analysis, regression analysis, or mapping).
  5. Discuss about the following questions. Will a policy aiming to improve jobs-housing balance in urban areas have significant effects on average commuting time in the region? Do you think job accessibility is a good performance measure for the regional land use and transportation system? A regional planning agency may want to estimate regional level accessibility index regularly (e.g. biannually) to monitor changes in the regional level accessibility. Do you think the accessibility measure that you have estimated can serve this purpose? What modification of the accessibility measure would you consider? To answer this question, you may have to think about how employment growth in the region would affect this accessibility measure.
  1. Deliverables:
  2. Submit a MS-Word file of 3 to 5 page essay that presents a summary of your analysis results and answers to the questions above to the Canvas assignment page.
  3. Also submit your MS-Excel file to the Canvas assignment page.

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