Generate Isochrones

PyPI

The Python package manager, pip, is required to install the API from PyPI.

1. Install the API:

   1	pip install gpudb --upgrade

2. Test the installation:

   1	python -c "import gpudb;print('Import Successful')"

   If Import Successful is displayed, the API has been installed as is ready for use.

Git

1. In the desired directory, run the following, but be sure to replace <kinetica-version> with the name of the installed Kinetica version, e.g., v7.2:

   1	git clone -b release/<kinetica-version> --single-branch https://github.com/kineticadb/kinetica-api-python.git

2. Change directory into the newly downloaded repository:

   1	cd kinetica-api-python

3. In the root directory of the unzipped repository, install the Kinetica API:

   1	sudo python setup.py install

4. Test the installation (Python 2.7 (or greater) is necessary for running the API example):

   1	python examples/example.py

Data File

The example script references the dc_shape.csv data file, mentioned in the Prerequisites, in the current local directory, by default. This directory can specified as a parameter when running the script.

Connections

This example will use two connections, one to the database and one to the WMS endpoint, configured as follows:

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kinetica = gpudb.GPUdb(host = [args.url], username = args.username, password = args.password)

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WMS_URL = args.url + "/wms"

Constants

Many constants are defined at the beginning of the script:

• CSV_ROW_SIZE -- the expected row number for the CSV file
• OPTION_NO_ERROR -- reference to a /clear/table option for ease of use and repeatability
• OPTION_ISO -- a Python dictionary containing /visualize/isochrone options key-value pairs
  1	OPTION_ISO = {"concavity_level": "0.2", "is_replicated": "true"}

• OPTION_ISO_CONTOUR -- a Python dictionary containing /visualize/isochrone contour_options key-value pairs
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  OPTION_ISO_CONTOUR = { "adjust_grid": "false", "adjust_levels": "false", "gridding_method": "INV_DST_POW", "labels_font_size": "16", "labels_font_family": "Sans", "labels_search_window": "4", "grid_size": str(100), "min_grid_size": "10", "max_grid_size": "300", "search_radius": "9", "smoothing_factor": "0.000001", "color_isolines": "true", "width": "512", "height": "-1" }

• OPTIONS_ISO_STYLE -- a Python dictionary containing /visualize/isochrone style_options key-value pairs
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  OPTION_ISO_STYLE = { "line_size": "2", "color": "0xFF000000", "bg_color": "0x00000000", "colormap": "jet", "text_color": "0xFF000000" }

• SCHEMA -- the name of the schema in which the tables will be created
• TABLE_DC -- the name of the table into which the DC Shape dataset is loaded
• TABLE_D_LVL -- the name of the table into which the IAD isochrone contour levels are output
• TABLE_JOIN -- the name of the join view hosting the shared area between the three isochrone contour level output tables
• TABLE_K_ISO_SOLVE -- the name of the table into which the WMS examples' isochrones solution (the position and cost for each vertex in the underlying graph) is output
• TABLE_K_LVL1 -- the name of the table into which the Kinetica isochrone (leading from) contour levels are output
• TABLE_K_LVL2 -- the name of the table into which the Kinetica isochrone (leading to) contour levels are output
• TABLE_R_LVL -- the name of the table into which the DCA isochrone contour levels are output
• GRAPH_DC -- the underlying DC shape graph that powers the isochrone calculation
• D_AIR, K_HQ, R_AIR -- the source nodes for the IAD, Kinetica HQ, & DCA isochrone requests, respectively
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  D_AIR = "POINT(-77.446831 38.955309)" K_HQ = "POINT(-77.115203 38.881578)" R_AIR = "POINT(-77.042295 38.849684)"

• D_AIR_IMG, K_HQ_IMG1, K_HQ_IMG2, R_AIR_IMG -- the resulting generated isochrone image from the IAD, Kinetica HQ (leading from), Kinetica HQ (leading to), & DCA isochrone requests, respectively

Graph Creation

One graph is used for the isochrones examples utilized in the script: dc_shape_graph, a graph based on the dc_shape dataset (the CSV file mentioned in Prerequisites).

The dc_shape_graph is created with the following characteristics:

• It is directed because the roads in the graph have directionality (one-way and two-way roads)
• It has no explicitly defined nodes because the nodes are not used in these examples
• The edges are represented using WKT LINESTRINGs in the shape column of the dc_shape table (WKTLINE). Each edge's directionality is derived from the direction column of the dc_shape table (DIRECTION).
• The weights represent the time taken to travel the edge, which is derived from the length of the edge divided by the speed column values also found in the dc_shape table. This derived time value is mapped to the VALUESPECIFIED identifier.
• It has no inherent restrictions for any of the nodes or edges in the graph
• It will be replaced with this instance of the graph if a graph of the same name exists (recreate)
  Create Graph

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  create_graph_dc_resp = kinetica.create_graph( graph_name = GRAPH_DC, directed_graph = True, nodes = [], edges = [ TABLE_DC + ".direction AS DIRECTION", TABLE_DC + ".shape AS WKTLINE" ], weights = [ TABLE_DC + ".direction AS EDGE_DIRECTION", TABLE_DC + ".shape AS EDGE_WKTLINE", "ST_LENGTH(" + TABLE_DC + ".shape,1)/" + TABLE_DC + ".speed AS VALUESPECIFIED" ], restrictions = [], options = {"recreate": "true"} )

The graph output to WMS:

Visualizing Isochrones

First, additional contour and isochrones options are provided:

• add_labels is set to true to display labels on the isochrones image
• projection is set to web_mercator to change the spatial reference system
• solve_direction is set to to_source to calculate isochrones leading to IAD

Then, the first group of isochrones is calculated using the following parameters and an image is generated using /visualize/isochrone endpoint:

• source_node is provided as a WKT point (D_AIR) paired with a NODE_WKTPOINT identifier
• max_solution_radius is set for 2400 seconds' (or 40 minutes') worth of travel surrounding the source node
• num_levels is set to 1 to only display one isochrone contour around the source node
• generate_image is set to True to generate an image and output it to the response

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vis_isochrone_d_resp = kinetica.visualize_isochrone(
    graph_name = GRAPH_DC,
    source_node = D_AIR + " AS NODE_WKTPOINT",
    max_solution_radius = 40 * 60,
    num_levels = 1,
    generate_image = True,
    levels_table = TABLE_D_LVL,
    style_options = OPTION_ISO_STYLE,
    contour_options = OPTION_ISO_CONTOUR,
    options = OPTION_ISO
)

The image is pulled from the response and written locally:

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img = vis_isochrone_d_resp["image_data"]
img_isochrones_to_dulles = open(D_AIR_IMG, "wb")
img_isochrones_to_dulles.write(img)
img_isochrones_to_dulles.close()

The generated image for IAD isochrones:

The second group of isochrones is calculated using the following parameters:

• source_node is provided as a WKT point (R_AIR) paired with a NODE_WKTPOINT identifier
• max_solution_radius is set for 1500 seconds' (or 25 minutes') worth of travel surrounding the source node
• num_levels is set to 1 to only display one isochrone contour around the source node
• generate_image is set to True to generate an image and output it to the response

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vis_isochrone_r_resp = kinetica.visualize_isochrone(
    graph_name = GRAPH_DC,
    source_node = R_AIR + " AS NODE_WKTPOINT",
    max_solution_radius = 25 * 60,
    num_levels = 1,
    generate_image = True,
    levels_table = TABLE_R_LVL,
    style_options = OPTION_ISO_STYLE,
    contour_options = OPTION_ISO_CONTOUR,
    options = OPTION_ISO
)

The image is pulled from the response and written locally:

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img = vis_isochrone_r_resp["image_data"]
img_isochrones_to_reagan = open(R_AIR_IMG, "wb")
img_isochrones_to_reagan.write(img)
img_isochrones_to_reagan.close()

The generated image for DCA isochrones:

Before the last example, the solve direction is updated to reflect that the isochrones are calculated leading from Kinetica HQ:

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OPTION_ISO["solve_direction"] = "from_source"

Finally, the third group of isochrones can be calculated using the following parameters:

• source_node is provided as a WKT point (K_HQ) paired with a NODE_WKTPOINT identifier
• max_solution_radius is set for 600 seconds' (or 10 minutes') worth of travel surrounding the source node
• num_levels is set to 1 to only display one isochrone contour around the source node
• generate_image is set to True to generate an image and output it to the response

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vis_isochrone_k1_resp = kinetica.visualize_isochrone(
    graph_name = GRAPH_DC,
    source_node = K_HQ + " AS NODE_WKTPOINT",
    max_solution_radius = 10 * 60,
    num_levels = 1,
    generate_image = True,
    levels_table = TABLE_K_LVL1,
    style_options = OPTION_ISO_STYLE,
    contour_options = OPTION_ISO_CONTOUR,
    options = OPTION_ISO
)

The image is pulled from the response and written locally:

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img = vis_isochrone_k1_resp["image_data"]
img_isochrones_from_kinetica = open(K_HQ_IMG1, "wb")
img_isochrones_from_kinetica.write(img)
img_isochrones_from_kinetica.close()

The generated image for Kinetica HQ (leading from) isochrones:

To find the shared area between the three contours that were just created, the ST_INTERSECTION() geospatial function is used to join the three tables together:

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create_join_resp = kinetica.create_join_table(
    join_table_name = TABLE_JOIN,
    table_names = [
        TABLE_D_LVL + " AS DULLES",
        TABLE_K_LVL1 + " AS KINETICA",
        TABLE_R_LVL + " AS REAGAN"
    ],
    column_names = [
        "ST_INTERSECTION(REAGAN.Isochrones, ST_INTERSECTION(KINETICA.Isochrones, DULLES.Isochrones)) AS shared_isochrones"
    ],
    expressions = [],
    options = {}
)

The shared area geometry output to WMS:

The three isochrones overlaid on top of each other:

• Red signifies being within 40 minutes of IAD
• Blue signifies being within 25 minutes of DCA
• Purple signifies being within 10 minutes of Kinetica HQ

Visualizing Isochrones Using WMS

To visualize isochrones via the /wms endpoint, the WMS payload must be constructed. Consult /wms for more information on WMS parameters, styles, and style options.

WMS payloads require a small set of standard parameters that will generally be the same across all WMS requests:

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"request": "GetMap",
"version": "1.1.1",
"format": "image/png",

The next settings inform WMS to generate isochrones (and to make Isochrones parameters available for use) and to set the image width to 512 pixels. The image height is set to -1, which the database will replace with the value resulting from multiplying the aspect ratio by the image width.

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"styles": "isochrone",
"image_width": 512,
"image_height": -1,

The following parameters define how the isochrones will be calculated. Notice these are similar to the previous examples:

• source_node is provided as a WKT point (K_HQ) paired with a NODE_WKTPOINT identifier
• max_solution_radius is set for 900 seconds' (or 15 minutes') worth of travel surrounding the source node
• num_levels is set to 10 to 10 isochrone contours around the source node
• generate_image is set to True to generate an image and output it to the response

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"graph_name": GRAPH_DC,
"source_node": K_HQ + " AS NODE_WKTPOINT",
"max_solution_radius": "900",
"num_levels": "10",
"generate_image": "true",
"levels_table": TABLE_K_LVL2,

Isochrone style options are passed in using the constants:

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"line_size": OPTION_ISO_STYLE["line_size"],
"color": OPTION_ISO_STYLE["color"][2:],  # remove the 0x
"bg_color": OPTION_ISO_STYLE["bg_color"][2:],  # remove the 0x
"colormap": OPTION_ISO_STYLE["colormap"],

Isochrone contour options are passed in. Additional contour style parameters are passed in for additional customization over the generated isochrones:

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"projection": "plate_carree",
"search_radius": OPTION_ISO_CONTOUR["search_radius"],
"color_isolines": OPTION_ISO_CONTOUR["color_isolines"],
"add_labels": "false",
"gridding_method": OPTION_ISO_CONTOUR["gridding_method"],
"smoothing_factor": OPTION_ISO_CONTOUR["smoothing_factor"],
"max_search_cells": "100",
"min_grid_size": OPTION_ISO_CONTOUR["min_grid_size"],
"max_grid_size": OPTION_ISO_CONTOUR["max_grid_size"],

Lastly, isochrone options are passed in. Note the change in direction for the solve_direction as we want isochrones leading to Kinetica HQ in this instance.

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"solve_table": TABLE_K_ISO_SOLVE,
"is_replicated": OPTION_ISO["is_replicated"],
"concavity_level": OPTION_ISO["concavity_level"],
"solve_direction": "to_source"

The WMS request is sent and the image is pulled from the response and written locally:

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params = urllib.urlencode(payload)
call_wms_request = requests.get(WMS_URL, auth=(username, password), params=params)
wms_img = call_wms_request.content

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img_isochrones_to_kinetica = open(K_HQ_IMG2, "wb")
img_isochrones_to_kinetica.write(wms_img)
img_isochrones_to_kinetica.close()

The generated image for Kinetica HQ (leading to) isochrones:

With labels and map data underneath:

Tip

Using the solve_table (TABLE_K_ISO_SOLVE), you can class break on the z (cost) column in WMS to see individual points and their relative cost: