research data and programming files

It is the implementation process description for measuring spatial accessibility to facilities based on multiple transportation modes considered residential transportation modes choices (MTM-RTMC).

#### 1. Prerequisites for operation

​ (1) We should sign up for Amap Maps account and get the key from Amap Developer Center through URL https://lbs.amap.com/

​ (2) The required basic software environments are: Python 3.6, ArcGIS 10.2, Oracle 10.1, MongoDB 3.1.

​ (3) In this experiment, the MongoDB collection data, "rtmcp_mtm_gm.json" data, should be restored in the MongoDB database. The import command is as "mongoimport --db test --collection rtmcp_mtm_gm --file D:\ rtmcp_mtm_gm.json". The basic data structure of demand object stored in "rtmcp_mtm_gm" collection is as following in **Appendix A**.

​ (4) In this experiment, the spatial statistical units, "street_block.shp" data, should be restored into the Oracle database through ArcGIS SDE. Creating a geodatabase in Oracle can refer to URL "". The "street_block.shp" data is replaced by a mock data of my spatial statistical units experimental data, called "street_block_mock_data.shp" data. The "street_block_mock_data.shp" data inherit all fields from my spatial statistical units experimental data.

​ **Why we use mock data to express our spatial statistical units data?**

​ Our spatial statistical units data, street block data in Nanjing, involve with mapping geographic information security. The street block data is similar to traffic analysis area (TAZ) data, very detailed.

​ **The generation process for "street_block_mock_data.shp" data in ArcMap is as following:**

​ [data]street block polygon data --->[arcmap tool operating]feature to point ---> [data]street block central points data ---->[arcmap tool operating]buffer ---> [data] cricle polygons data --->[arcmap tool operating]feature envelope to polygon ---> [data] street block mock data.

​ **The fields description of the "street_block_mock_data.shp" data is as following:**

​ OBJECTID_12 - the key field of the street_block_mock_data.shp data;

​ OBJECTID - the key field of the street_block.shp data.

​ SUM_ACTURA - the population count of the street block.

​ X - the location x of the street block's central point.

​ Y - the location y of the street block's central point.

​ SINGLE - the accessibility value calculated by the STM G2SFCA model.

​ MTM - the accessibility value calculated by the MTM G2SFCA model.

​ MTM_RTMCP - the accessibility value calculated by the MTM_RTMCP G2SFCA model.

#### 2. The code implementation

**Step 1:** We initialize the "rtmcp_mtm_gm" collection through _01_initial_mogodb_rtmcp_mtm_gm_collection.py.

**Step 2:** A method for real-time navigation time and distance between demand points and provider points is developed based on the Amap Maps route planning API.

Four route planning data of MTM from internet maps include driving, walking, public transportation, and bicycling.

_02_route_plan_calculator.py

_02_route_planner.py

_02_route_planning_queue.py

_02_save_route_planning_data_for_queue.py

**Step 3:** We design three accessibility models, including the STM G2SFCA, the MTM G2SFCA, and the MTM-RTMC G2SFCA, and calculate the accessibility results of the three models.

_03_calculate_softmax_weights_for_RTMCP_MTM.py

_03_STM_G2SFCA_calculation.py

_03_MTM_G2SFCA_calculation.py

_03_MTM_RTMC_G2SFCA_calculation.py

**Step 4:** To explain the experimental results intuitively, we need to express the results spatially by those coding files,including:

_04_extract_accessibility_valut_to_ArcSDE_shp.py

_04_extract_NCHGZ_distance_time_to_shp.py

The mapping symbolization process was completed by ArcMap10.2.

In addition, we developed a series of basic functions to support the implementation, such as: the function to operate MongoDB, mongo_operater.py