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GSOC 2020 project report: Raster Awareness in PySAL

GSoC 2020 Project Report by Mragank Shekhar

Project Details

Field Value
Project Title Raster Awareness in PySAL
Project Link https://summerofcode.withgoogle.com/projects/#5775104799145984
Organization NumFOCUS (Sub-Org: PySAL)
Mentors Stefanie Lumnitz, Dani Arribas-Bel, Levi John Wolf
Contributed Repo libpysal, splot
Working Repo Mgeeeek/libpysal
Gitter Room https://gitter.im/GSoC2020-PySAL/Raster_awareness_in_PySAL
Project Blog https://mgeeeek.github.io/tags/gsoc/

Project Description

From early on, PySAL was designed with the focus of performing vector-based spatial analysis and therefore it didn't have the tools to handle the input-output of large raster data. This restricted some folks in the geospatial community to use PySAL's analytical functionality over raster data. Furthermore, in recent years several geographic data organizations started releasing data in raster format which earlier came in vector format mostly because of advancement in computational capabilities and high storage availability. This led to an increase in the demand for the functionality offered by PySAL to make it work with raster data.

Taking this into consideration, my main motive was to design and implement a lightweight interface which will provide the functionality for streamlining raster data access and making it more accessible to build the data structure accepted by the analytical methods of the PySAL library (mainly libpysal.weights.W/WSP objects) from accessed raster data (which will be an instance of xarray.DataArray). Ultimately, this functionality will open up the use of analytical methods like esda, spatial regression over raster data.

Following were the major milestones that we planned to achieve for successfully building raster interface:

  • Design quality APIs which followed existing structure used across PySAL.
  • Deal with different properties of raster including missing data values, different data alignment, and multi-layer data.
  • Add transformational methods to support conversion to and from PySAL weights object and xarray.DataArray object.
  • Integrate the interface with PySAL without causing any disturbance to other parts of the library.
  • Study performance and memory efficiency of the conversion methods and optimize the interface to support computations for large raster datasets.
  • Add proper documentation, tests, and an example notebook.

Since in this report we'll talk a lot about raster data and weights object, let's look at the visualization of these 2 things. Weights object in PySAL is a graph like structure which expresses the relationship of a spatial point with its neighboring points, whereas we can think of a raster as an n-d array. We can now take a look at the visual example below: Fig. 1 Fig. 2 Here, fig. 1 represents the original raster dataset which was then used to derive the sliced raster shown in fig. 2. Furthermore, we can see 2 plots of weights objects which are represented by fig. 3 and fig. 4. These weights objects were obtained using the sliced raster. We can see how the relationship of spatial points is different in Rook and Queen contiguity. Raster interface dealt with nodatavals (missing/NaN values) while creating the weights object that's why we are seeing those empty pixels.

Coming back to the report, the development timeline for building the project was divided into 3 phases:

  1. API design phase
  2. Integration phase
  3. Optimization phase

API design phase

Quick Overview

Main goals achieved:

  • Studied data structures provided by xarray and PySAL's W and WSP objects.
  • Created an initial skeletal implementation of the interface.
  • Iteratively designed and experimented with different APIs structures.
  • Created a working prototype of the Base Raster Interface.

Pull Requests, Commits and Issues created:

Blog posts published:

Contents:

  1. I/O dependency for accessing raster
  2. Planning main functionality for the interface
  3. Designing interface's API (weights builder)
  4. Designing interface's API (return journey to xarray.DataArray)
  5. Iterative approach for API design

Detailed Summary

  1. I/O dependency for accessing raster

    The original plan involved using rasterio as the main library for raster data access but from the start of the community bonding period, my mentors suggested a shift from rasterio to xarray. After going through the documentation of xarray it felt superior in terms of usability and accessibility as the features it provided outweighed that of rasterio and it also featured rasterio backend to access the raster data formats easily. Moreover, building the interface to work with the data structure provided by the xarray library made the project available to a broader audience. Therefore I continued with the xarray or xarray.DataArray to be specific, as users can easily obtain xarray.DataArray object from raster data format using xarray's I/O functionality which can read from a variety of data formats some of them are listed below:

  2. Planning main functionality for the interface

    After understanding the structure of xarray.DataArray, it was time to create an initial skeletal implementation of the interface which provided details of the functionality that the interface will offer. As described by Dani in this thread:

    • The interface would provide the functionality to create both weights object libpysal.weights.W & WSP from user-provided data though xarray.DataArray object.
    • It should also support the return journey of the data obtained after an analytical operation on the weights object to xarray.DataArray.
  3. Designing interface's API (weights builder)

    Once the main functionality was finalized, we jumped on to the API designing part of the project. My mentors explained how PySAL was inclined towards an object-oriented paradigm and the majority of the operations are achieved through creating an instance of a particular object using the class method and then calling the instance method. But it was also in the transitioning phase of incorporating functional programming though it was not on the top of their priority list. This motivated us to provide the functionality of direct function calling to convert DataArray and create respective weights object, but also implement these methods as a class method using contiguity classes (Rook and Queen).

    from libpysal.weights import raster, Rook
    import xarray as xr
    
    # Accessing raster data using xarray
    da = xr.open_rasterio("raster.tif")
    
    # Method 1
    wsp1 = raster.da2WSP(da, criterion="rook")
    data1 = da.to_series()[wsp1.index]
    
    # Method 2
    wsp2 = Rook.from_xarray(da, sparse=True)
    data2 = da.to_series()[wsp2.index]
    
    data1.equals(data2)
    True
    
  4. Designing interface's API (return journey to xarray.DataArray)

    In the above example, .from_xarray method uses direct methods like da2W or da2WSP under the hood to build weights object from xarray.DataArray. A new module called raster was introduced to hold all the functionalities offered by the interface, it also helped in structuring the interface by separating it with other areas of the library. Just like what we saw with .from_xarray method, all the work related to the raster conversion is dispatched to one of the methods in the raster module.

    A similar approach was taken for designing the API for the return journey. We implemented 2 types of methods:

    • toDataArray which can be called directly.
    • Another way was to call the instance method .to_xarray which provided similar functionality to the direct methods.

    In the later phase, .to_xarray method was removed due to several limitations because the structure of created weights object was different from the normal ones (more on this later), and therefore these methods would fail if the user passed the weights object not created using the interface.

  5. Iterative approach for API design

    It took quite a few iterations to finalize the correct API of the base interface. Below are the early pull requests that I pushed to get feedbacks for the API of the transformational functions. These are far from clean as I was still experimenting with different design decisions.

    Commit 9eb8311 was the point of the 1st phase where the majority of the APIs were locked. There were still some areas left where the community was divided particularly in structuring and naming the parameters of the methods, but these were minor issues that were solved in the later stages.

Integration phase

Quick Overview

Main goals achieved:

  • Restructured interface by aligning the methods with future weights object design.
  • Made toDataArray method work independent of raster metadata.
  • Added KNN weights support to the interface.
  • Fixed documentation, by following the NumPy-Doc convention for docstrings.
  • Added unit-tests and an example notebook.
  • Explored different ways to optimize the interface.

Pull Requests, Commits and Issues created:

Blog posts published:

Contents:

  1. Refactoring weights object
  2. Refactoring toDataArray method
  3. Adding distance-based weights object support
  4. Finalzing base raster interface
  5. Identifying areas to optimize

Detailed Summary

  1. Refactoring weights object

    In the second phase, we focused on how to integrate the interface with the libpysal. One major issue related to the integration was that the output weights of the transformation methods (da2W/da2WSP) were different from the normal weights object, this was due to the extra attributes added to the weights object namely attrs and coords. This addition was implemented in the builder method to make the weights object more interactable with xarray.DataArray as the user will want to access the raster data easily after building the weights from the DataArray. Therefore coords attribute stored the coordinates indices of non-missing values in the form of pandas.MultiIndex, and attrs stored the metadata which was required when going back to the xarray.DataArray. This issue was addressed in the community meeting. After a detailed meeting with community members regarding the weights object, one of the major change that was proposed was to shift towards WSP object (here are the meeting notes). Additionally, the id_order attribute will be replaced by index which will contain an instance of pandas.Index aligned with the ids of the weight object.

    This required a few changes in my existing implementation:

    • Renamed coords to index and removed attrs attribute.
    • Added index parameter in the __init__ method of WSP class.
    • Fixed creation of W from xarray.DataArray which now only relies on da2WSP method.
  2. Refactoring toDataArray method, making it work with only weights object and independent of metadata

    A few of the major changes added during the second phase involved the toDataArray method. To make the interface more accessible we decided to only build the DataArray from weights object and data values aligned to the weights object. This was tricky as the weights object does not contain any information related to the missing values except the coordinates in the index attribute. After several tries of my own, I shifted towards the source code of the pandas.MultiIndex. From my earlier impressions, the data structure of pandas.MultiIndex was very different. Once I understood the architecture of pandas.MultiIndex I pushed a patch with the required changes, this patch refactored the logic and used the codes and levels property to construct all the coordinates of the DataArray. Commit e5cd4e6 reflects the changes made targeting this method. Later toDataArray was split into 2 separate functions w2da and wsp2da. We can see the use of this functionality below:

    from libpysal.weights import raster, Queen
    
    # Creating test DataArray
    da = raster.testDataArray(
        shape=(10, 56, 49),
        time=False,
        rand=False,
        missing_vals=True
    )
    
    wsp = Queen.from_xarray(
        da=da,
        z_value=5,
        coords_label=None,
        sparse=True
    )
    
    # Accessing data from da using index attribute
    data = da.to_series()[wsp.index]
    
    # Converting back to the DataArray
    da_back = wsp2da(data, wsp)
    da_back
    <xarray.DataArray (band: 1, y: 56, x: 49)>
    -1 211 -1 -1 -1 41 37 -1 62 -1 ... -1 171 118 135 28 151 -1 -1 -1 146
    Coordinates:
    * band     (band) int64 5
    * y        (y) float64 66.1 66.2 66.3 66.4 66.5 ... 71.2 71.3 71.4 71.5 71.6
    * x        (x) float64 92.1 92.2 92.3 92.4 92.5 ... 96.5 96.6 96.7 96.8 96.9
    Attributes:
        nodatavals:  (-1,)
    
  3. Adding distance-based weights object support

    Rook and Queen contiguities were not sufficient when considered for further analysis on a raster data because Rook contiguity provided max 4 neighbors and Queen provided max 8. To tackle this, the next step was to implement distance weight builder either KNN or DistanceBand. We started with KNN, the initial implementation of KNN was very straight forward, we first converted cell centroids into points and then shipped them directly into the KNN builder which is based on KDTree. There were 2 major issues with this implementation first was very obvious as raster provided a regular lattice-like structure, KNN could have been more optimized since there was no need for building KDTree. Though the major issue was related to the ordering of the selected neighbors. Since in raster more than 2 neighbors can be equidistant from the focal point, the method inconsistently selected neighbors in a different environment. Therefore this was later removed from the project.

  4. Adding tests, documentation, example notebook, and finalizing base raster interface

    Once all the components of the base raster Interface were finalized, we started working on the documentation and tests. From the beginning I followed the style of other methods inside libpysal when creating docstrings, luckily my mentors notified me early on to follow NumPy-Doc convention for docstrings. After fixing docstring we worked on adding unit-tests since we were not storing any raster dataset inside the project therefore we decided to create a testDataArray method (usage provided in the above example) which provided us with the different dataset to test interface correctly.

    I also added an example notebook that introduced the API of the interface and gave a brief overview of its use-case in different scenarios. The notebook can be accessed from here:

    Commits 6565dd0 to 94ec4f4 contains the majority of work finalized in the second phase. Since the main API of the base interface was finalized, I created a pull request targeting the master branch of libpysal:

    Note: All my main work for the first and second phase lives inside this pull request. The commit history doesn't look clean as for some reason I forced pushed a few changes.

  5. Identifying areas to optimize for adding support for large rasters.

    Along with working on the interface we were also identifying and exploring the different optimization methods to optimize the interface. This thread documents list of ideas we went through during exploration.

Optimization phase

Quick Overview

Main goals achieved:

  • Shifted away from lat2SW-based weight builder.
  • Redesigned sparse matrix builder based on COO_matrix.
  • Numba-fied all possible methods using njit decorator.
  • Added multi-threaded implementation for WSP builder using joblib.
  • Added higher_order functionality to the modified weight builder.
  • Added raster-based weights object support inside plot_spatial_weights method in splot.

Pull Requests, Commits and Issues created:

Blog posts published:

Contents:

  1. Moving on from lat2SW-based sparse weight builder
  2. Optimizing the addition of higher-order neighbors
  3. Adding visualization support for the interface

Detailed Summary

  1. Moving on from lat2SW-based (which uses dia_matrix) to new coo_matrix-based sparse weight builder

    Following the idea-collection issue, the first target for optimizing the interface was the weight builders. The existing approach used the lat2SW method which provided the main functionality for constructing a sparse matrix for the regular lattice. The main issue while using lat2SW was when we’re dealing with data consisting of missing values, in this case, boolean indexing was used for removing missing rows and columns from the created sparse matrix. The performance takes a hit due to boolean indexing of csr_matrix and this raised concerns for building weight objects for larger data. Memory consumption in lat2SW is also high due to the use of lists instead of np.array.

    A proper explanation of this issue and how it was solved is given in this blog post.

    Also, check out this notebook for a performance overview. These enhancements provided huge benefits both in terms of memory efficiency and faster performance. This also made the interface usable with large datasets.

  2. Optimizing the addition of higher-order neighbors

    In the previous section, I explained how KNN-based weight object was not very consistent when dealing with the raster-based data. Therefore, we decided to implement higher_order functionality, and 2 approaches were taken when adding the higher-order neighbors:

    • Implementing the first approach was relatively easier than the second approach, as only a few changes were added inside the modified weights builder. An extra argument was provided which acted as a threshold and then using modular arithmetic conditions the algorithms added the neighbors lying inside the threshold radius also following contiguity criterion, though this implementation was different as it added higher order neighbors even if the lower order neighbors were missing and therefore it was more like DistanceBand. This thread provides some insightful discussion related to how DistanceBand behaves with raster data:

    • As for the second approach, we were able to implement a correct algorithm for higher_order functionality inside weight builder, this algorithm used recursion to traverse nth-order neighbors or nodes because raster can be assumed as a graph. Unfortunately, we could not incorporate numba and multi-threading to the method and therefore the algorithm was very inefficient. But after some testing, we were able to add a proper algorithm for using the method that Martin suggested in this thread.

    Since both approaches are different from each other we added include_nas argument which if True, computes weights using the first approach, and if False the builder uses the second approach. We can take a look at the visual example below which explains this more intuitively.

    Here we used testDataArray method to create a small raster data filled with random and missing values. Fig. 6 shows the Rook contiguity with k=1 which means all first-order rook neighbors are selected. Fig. 7 shows the Rook contiguity with k=2 but with include_nas=False which means all non-missing first and second-order rook neighbors are selected (this uses the second approach as described previously). include_nas is True for Fig. 8, we can see that in this figure all the first order neighbors are selected but while selecting second order neighbors, the method takes consideration of (missing + nonmissing) first-order neighbors. Fig. 3 Fig. 4 After completion of these methods, a pull request was created targeting the master branch of libpysal which added optimized weights builder and higher_order functionality. This pull request describes how these approaches are different from each other:

This marked the end of the optimization phase.

  1. Adding visualization support for the interface.

    Along with optimization work, we also worked on adding the support for plotting functionality for a raster-based weight object. This required some changes inside the plot_spatial_weights method, mainly making it work with sparse weight objects instead of dense weight objects and adding a da argument for passing DataArray object.

    The visualization aspect of the project is still in progress, once this pull request gets reviewed. I'll add more enhancements which are listed in the issue.

Next Phase (post-gsoc)

  • Since the weight object is being refactored to remove the legacy part and adopt new features from newly available libraries like pandas and xarray. I'll need to reflect these changes to the interface once they are incorporated into the libpysal.
  • Additionally, the interface once merged will be in the alpha-testing phase and I'm expecting new bugs to pop-in which we were not able to test. Therefore I'll have to be available to provide the bug fixes for a few initial months of merging the interface.
  • However optimized the weights builder can be, there will be still a chance that the program will run out of memory as the raster size increases. One ambitious idea when working on the interface was related to lazily building the weights object. I did some early explorational prototyping for this part ([WIP] : Lazy weights object for raster interface) but it'll require a massive change in the structure of the weights object probably a new class only supporting lazy operations. Once it is successfully implemented all the conversion and computation will be lazy which will allow users to use larger than memory rasters, albeit the computations will be slow.
  • Even though xarray provides great support for Dask, the project has not yet taken advantage of or support dask structures. It'll be a nice functionality to add to the interface in the future.

Huge thanks to PySAL Community

As a computer science undergrad this project was a breath of fresh air for me since I was relatively new to the PySAL library and the field of geospatial data science. I gained lots of experience working on this project especially working on somethings I wasn't completely familiar with.

I like to thank my mentors Stefanie, Dani, Levi for taking the time to review the prs, clearing my doubts, and providing me with valuable feedback which helped me along the way of building this project. I also appreciate the work of other community members working on this cool library.

Now I'll meet you in the new season of surviving 2020 (post-gsoc edition).

Mragank Shekhar

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